There are evidence showing that meteorological factors, such as temperature and humidity, have critical effects on transmission of some infectious diseases, while quantifying the influence is challenging. In this study we develop a learning-explaining framework to discover the particular dependence of transmission mechanisms on meteorological factors based on multiple source data. The incidence rate based on the epidemic data and epidemic model is theoretically identified, and meanwhile the practical discovery of particular formula is feasible through deep neural networks (DNN), symbolic regression (SR) and sparse identification of nonlinear dynamics (SINDy). In particular, we initially learn the incidence rate in an SIRS model based on epidemic data, then use mechanism discovery methods to explore the possible explicit forms of the incidence rate, and consequently explore the possible relationship between transmission rate and meteorological factors. We finally use information criteria and a definition of evaluation score to make model selection, and hence suggest the optimal explicit formula. We illustrate the idea by derive the incidence rate and transmission rate of respiratory infectious diseases based on the case data on influenza-like illness (ILI) in Xi'an, Shaanxi Province of China and meteorological data from 1st January 2010 to 10th November 2016. The finding reveals that the influence of meteorological factors on transmission exhibits very strong nonlinearity, and modeling the effect should be of great care.

Viral respiratory illnesses are the most common acute illnesses experienced and generally follow a predicted pattern over time. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic interrupted that pattern.

The HIVE (Household Influenza Vaccine Evaluation) study was established in 2010 to follow a cohort of Southeast Michigan households over time. Initially focused on influenza, surveillance was expanded to include other major respiratory pathogens, and, starting in 2015, the population was followed year round. Symptoms of acute illness were reported, and respiratory specimens were collected and tested to identify viral infections. Based on the known population being followed, virus-specific incidence was calculated.

From 2015 to 2022, 1755 participants were followed in HIVE for 7785 person-years with 7833 illnesses documented. Before the pandemic, rhinovirus (RV) and common cold human coronaviruses (HCoVs) were the viruses most frequently identified, and incidence decreased with increasing age. Type A influenza was next but with comparable incidence by age. Parainfluenza and respiratory syncytial viruses were less frequent overall, followed by human metapneumoviruses. Incidence was highest in young children, but infections were frequently documented in all age groups. Seasonality followed patterns established decades ago. The SARS-CoV-2 pandemic disrupted these patterns, except for RV and, to a lesser extent, HCoVs. In the first 2 years of the pandemic, RV incidence far exceeded that of SARS-CoV-2.

Longitudinal cohort studies are important in comparing the incidence, seasonality, and characteristics of different respiratory viral infections. Studies documented the differential effect of the pandemic on the incidence of respiratory viruses in addition to SARS-CoV-2.

Anthropogenic land use impacts infectious diseases at the wildlife-domestic-human interface by changing host spatial distribution, behavior, density, and population dynamics. Canine distemper virus (CDV) is a significant cause of morbidity and mortality in many wild and domestic animals. Given the propensity of CDV to infect synanthropic mesocarnivores, it is important to investigate host and environmental factors affecting mesocarnivore CDV infection. Here, we investigated patterns of CDV infection and developed a statistical model to identify environmental variables related to CDV risk in commonly affected mesocarnivores. We sampled carcasses (N = 270) submitted to the Southeastern Cooperative Wildlife Disease Study from January 2019 to December 2022 and sequenced the CDV H-gene of 32 CDV-positive animals. Overall, 158 out of 270 mesocarnivores (58.5%) and four species (raccoon, red fox, gray fox, and striped skunk) were diagnosed with CDV across 13 states. Ripley's K analysis showed positive cases were more spatially clustered at larger distances than expected due to chance. A generalized linear model for CDV-infected animals showed surface imperviousness, precipitation, and subadult/adult age classes were significant positive explanatory variables, but elevation had a significant negative association with CDV infection likelihood. H-gene sequence diversity among wild mesocarnivores in the southeastern United States was geographically separated into groups east and west of the Mississippi River, with only two eastern samples clustering with western groups. By identifying areas of intense human development at the highest risk for CDV, it may be possible to focus surveillance efforts in these areas, allowing for earlier outbreak identification, potentially preventing cross-species CDV transmission.

Anthropogenic land use change can impact infectious disease spread by altering animal distribution and behavior. Canine distemper virus (CDV) is a significant cause of morbidity and mortality in wild and domestic carnivores. This study investigated how land use influences CDV infection in wild carnivores by examining tissues collected between 2019 and 2022 from wild carnivores found dead in the southeastern United States. CDV strains were geographically distinct, with differences between populations east and west of the Mississippi river. Statistical models showed areas with increased human development and higher precipitation had higher CDV risk; however, there was lower risk associated with higher elevations and younger animals. The importance of this study is that it identifies geographic structure of CDV in the southern United States, and identifies land-use associations with potential high-risk areas for CDV transmission-information that is useful for wildlife disease surveillance and control strategies.

An influenza forecasting system is critical to influenza epidemic preparedness. Low temperature has long been recognized as a condition favoring influenza epidemic, yet it fails to justify the summer influenza peak in tropics/subtropics. Recent studies have suggested that absolute humidity (AH) had a U-shape relationship with influenza survival and transmission across climate zones, indicating that a unified influenza forecasting system could be established for China with various climate conditions.

Our study has generated weekly influenza forecasts by season and type/subtype in northern and southern China from 2011 to 2021, using a forecasting system combining an AH-driven susceptible-infected-recovered-susceptible (SIRS) model and the ensemble adjustment Kalman filter (EAKF). Model performance was assessed by sensitivity and specificity in predicting epidemics, and by accuracies in predicting peak timing and magnitude.

Our forecast system can generally well predict seasonal influenza epidemics (mean sensitivity>87.5%; mean specificity >80%). The average forecast accuracies were 82% and 60% for peak timing and magnitude at 3-6 weeks ahead for northern China, higher than those of 42% and 20% for southern China. The accuracy was generally better when the forecast was made closer to the actual peak time.

The established AH-driven forecasting system can generally well predict the occurrence of seasonal influenza epidemics in China.

Although numerous studies have explored the complex relationship between air pollution, meteorological factors and respiratory infections, evidence for a mediating effect of air pollutants being involved in the association between meteorological factors and Influenza-like illness (ILI) is limited.

Correlations among ILI cases, air pollutants and meteorological factors were examined with Pearson correlation analyses. Further, we formulated six candidate mediation models to explore the mediating effect of air pollutant on the association between meteorological factors and ILI infections.

The meteorological factors minimum temperature/maximum humidity moderated by maximum humidity/minimum temperature and pressure directly affect ILI infections, and that some of meteorological factors can also indirectly affect them through air pollutants. Increases in maximum humidity and minimum temperature can directly reduce the numbers of ILI cases, or indirectly reduce them by reducing the concentration of air pollutants.

When the haze with low temperature, low humidity is forecasted by the meteorological agency, the environmental protection departments can take effective control measures to reduce the concentration of air pollutants, and public health departments should advocate human behavioral changes in order to mitigate and control ILI prevalence.

Climate and infectious diseases each present critical challenges on a warming planet, as does the influence of climate on disease. Both are governed by nonlinear feedbacks, which drive multi-annual cycles in disease outbreaks and weather patterns. Although climate and weather can influence infectious disease transmission and have spawned rich literature, the interaction between the independent feedbacks of these two systems remains less explored. Here, we demonstrate the potential for long-lasting impacts of El Niño-Southern Oscillation (ENSO) events on disease dynamics using two approaches: interannual perturbations of a generic SIRS model to represent ENSO forcing, and detailed analysis of realistic specific humidity data in an SIRS model with endemic coronavirus (HCoV-HKU1) parameters. Our findings reveal the importance of considering nonlinear feedbacks in susceptible population dynamics for predicting and managing disease risks associated with ENSO-related weather variations.

A pressing question resulting from global warming is how climate change will affect infectious diseases. Answering this question requires research into the effects of weather on the population dynamics of transmission and infection; elucidating these effects, however, has proved difficult due to the challenges of assessing causality from the predominantly observational data available in epidemiological research. Here we show how concepts from causal inference-the sub-field of statistics aiming at inferring causality from data-can guide that research. Through a series of case studies, we illustrate how such concepts can help assess study design and strategically choose a study's location, evaluate and reduce the risk of bias, and interpret the multifaceted effects of meteorological variables on transmission. More broadly, we argue that interdisciplinary approaches based on explicit causal frameworks are crucial for reliably estimating the effect of weather and accurately predicting the consequences of climate change.

Climate change and air pollution are pressing public health concerns, necessitating monitoring of their impact, particularly on respiratory diseases like obstructive lung diseases. This retrospective study analyzed medical records of patients hospitalized at the Warmia and Mazury Centre for Pulmonary Diseases in Olsztyn, Poland (2012-2021) for asthma and chronic obstructive pulmonary disease (COPD) exacerbations. Data included meteorological factors such as temperature, humidity, wind speed, precipitation, and levels of PM2.5 and PM10. The Humidex was utilized to assess thermal discomfort, considering various meteorological and thermal seasons. Findings indicated seasonal variability in asthma and COPD exacerbations. During winter, poorer air quality due to higher PM2.5 and PM10 levels correlated with increased exacerbations (r = 0.283, p < 0.05; r = 0.491, p < 0.001). In summer, discomfort from meteorological conditions led to more hospital admissions. Humidex values strongly correlated with admissions for obstructive diseases (R2 = 0.956 for asthma; R2 = 0.659 for COPD), with July and August showing statistically higher admission rates (p < 0.05). The study highlights the significant impact of air pollution and meteorological conditions on exacerbations of asthma and COPD, with Humidex serving as a valuable predictor during summer months.

Influenza forecasts could aid public health response as shown for temperate regions, but such efforts are more challenging in the tropics and subtropics due to more irregular influenza activities. Here, we built six forecast approaches for influenza in the (sub)tropics, with six model forms designed to model seasonal infection risk (i.e. seasonality) based on the dependence of virus survival on climate conditions and to flexibly account for immunity waning. We ran the models jointly with the ensemble adjustment Kalman filter to generate retrospective forecasts of influenza incidence in subtropical Hong Kong from January 1999 to December 2019 including the 2009 A(H1N1)pdm09 pandemic. In addition to short-term targets (one to four weeks ahead predictions), we also tested mid-range (one to three months) and long-range (four to six months) forecasts, which could be valuable for long-term planning. The largest improvement came from the inclusion of climate-modulated seasonality modelling, particularly for the mid- and long-range forecasts. The best-performing approach included a seasonal-trend-based climate modulation and assumed mixed immunity waning; the forecast accuracies, including peak week and intensity, were comparable to that reported for temperate regions including the USA. These findings demonstrate that incorporating mechanisms of climate modulation on influenza transmission can substantially improve forecast performance in the (sub)tropics.

Ambient temperature and humidity are established environmental stressors with regard to influenza infections; however, mapping disease burden is difficult owing to the complexities of the underlying associations and differences in vulnerable population distributions. In this study, we aimed to quantify the burden of influenza attributable to non-optimal ambient temperature and absolute humidity in Japan considering geographical differences in vulnerability.

The exposure-lag-response relationships between influenza incidence, ambient temperature, and absolute humidity in all 47 Japanese prefectures for 2000-2019 were quantified using a distributed lag non-linear model for each prefecture; the estimates from all the prefectures were then pooled using a multivariate mixed-effects meta-regression model to derive nationwide average associations. Association between prefecture-specific indicators and the risk were also examined. Attributable risks were estimated for non-optimal ambient temperature and absolute humidity according to the exposure-lag-response relationships obtained before.

A total of 25,596,525 influenza cases were reported during the study period. Cold and dry conditions significantly increased influenza incidence risk. Compared with the minimum incidence weekly mean ambient temperature (29.8 °C) and the minimum incidence weekly mean absolute humidity (20.2 g/m3), the cumulative relative risks (RRs) of influenza in cold (2.5 °C) and dry (3.6 g/m3) conditions were 2.79 (95% confidence interval [CI]: 1.78-4.37) and 3.20 (95% CI: 2.37-4.31), respectively. The higher RRs for cold and dry conditions were associated with geographical and climatic indicators corresponding to the central and northern prefectures; demographic, socioeconomic, and health resources indicators showed no clear trends. Finally, 27.25% (95% empirical CI [eCI]: 5.54-36.35) and 32.35% (95% eCI: 22.39-37.87) of all cases were attributable to non-optimal ambient temperature and absolute humidity (6,976,300 [95% eCI: 1,420,068-9,306,128] and 8,280,981 [95% eCI: 8,280,981-9,693,532] cases), respectively.

These findings could help identify the most vulnerable populations in Japan and design adaptation policies to reduce the attributable burden of influenza due to climate variability.

This study aims to examine the association between temperature variabilit (TV) exposure and influenza incidence in China, and the modification effect of PM2.5 levels. Data on daily influenza cases, weather conditions, and PM2.5 concentrations were collected from 339 cities across mainland China from 2014 to 2019. TV was computed as the standard deviation of daily maximum and minimum temperatures for the current day and the previous several days (i.e., TV0-1 to TV0-7). A space-time-stratified case-crossover design with conditional Poisson regression was employed. Overall, each 1 °C increase in TV0-6 was linked to 3.3 % (95 % CI: 3.1 %, 3.5 %) rise in influenza incidence, potentially attributing 14.73 % (95 % CI: 14.08 %, 15.37 %) of cases to this exposure. PM2.5 concentration showed substantial modification effect on the association, such that the relative risk (RR) of influenza incidence grew from 1.027 (95 % CI: 1.025, 1.029) to 1.040 (95 % CI: 1.038, 1.042) as PM2.5 levels increased from 15 to 75 μg/m³ . Females and individuals over 65 years old were more susceptible to TV exposure and the PM2.5 modification. Stronger effects were observed during cold season and in North region. The findings highlight the integrating considerations of TV and PM2.5 exposures into public health measures for influenza prevention and control.

While COVID-19 continues to challenge the world, meteorological variables are thought to impact COVID-19 transmission. Previous studies showed evidence of negative associations between high temperature and absolute humidity on COVID-19 transmission. Our research aims to fill the knowledge gap on the modifying effect of vaccination rates and strains on the weather-COVID-19 association.

Our study included COVID-19 data from 439 cities in 22 countries spanning 3 February 2020 - 31 August 2022 and meteorological variables (temperature, relative humidity, absolute humidity, solar radiation, and precipitation). We used a two-stage time-series design to assess the association between meteorological factors and COVID-19 incidence. For the exposure modeling, we used distributed lag nonlinear models with a lag of up to 14 days. Finally, we pooled the estimates using a random effect meta-analytic model and tested vaccination rates and dominant strains as possible effect modifiers.

Our results showed an association between temperature and absolute humidity on COVID-19 transmission. At 5 °C, the relative risk of COVID-19 incidence is 1.22-fold higher compared to a reference level at 17 °C. Correlated with temperature, we observed an inverse association for absolute humidity. We observed a tendency of increased risk on days without precipitation, but no association for relative humidity and solar radiation. No interaction between vaccination rates or strains on the weather-COVID-19 association was observed.

This study strengthens previous evidence of a relationship of temperature and absolute humidity with COVID-19 incidence. Furthermore, no evidence was found that vaccinations and strains significantly modify the relationship between environmental factors and COVID-19 transmission.

Bioaerosols, capable of transporting microorganisms, can impact human health and agriculture by spreading to nearby communities. Their transmissions are influenced by various factors, including weather conditions and human activities. However, the scarcity of detailed, taxon-specific data on bioaerosols' sizes limits our ability to assess risks associated with bioaerosols' generation and spread. This study examined the composition and size of bioaerosols at a livestock farm and a non-agricultural site, focusing on how bioaerosols evolve at different locations and meteorological conditions. The location had an impact on bioaerosol samples. We conducted 16S rRNA gene amplicon sequencing to identify bacteria genera in bioaerosols. We observed consistently higher concentrations of bioaerosols across all sizes at the livestock farm, and samples from the livestock farm exhibited greater bacterial diversity, where we identified Staphylococcus and Corynebacterium as the most abundant species. The effects of rainfall on bioaerosol diversity are complex, suggesting a dynamic interplay between bioaerosol removal and generation. After rainfall, the bioaerosol fraction of particles larger than 2.5 μm increased by nearly 400% compared to post-rain levels. Conversely, for bioaerosols below 1 μm size, the fraction decreased by 50%. Furthermore, the sequencing results showed that precipitation differentially responded to the abundance of various genera in the bioaerosols. Moreover, even for the same genus, the response to precipitation varied depending on the size of the bioaerosols. Our research reveals how size, location, and environmental conditions influence bioaerosol dynamics, enhancing our understanding of bioaerosol formation and transmission.

Pathogen-pathogen interactions represent a critical but little-understood feature of infectious disease dynamics. In particular, experimental evidence suggests that influenza virus and respiratory syncytial virus (RSV) compete with each other, such that infection with one confers temporary protection against the other. However, such interactions are challenging to study using common epidemiologic methods. Here, we use a mathematical modeling approach, in conjunction with detailed surveillance data from Hong Kong and Canada, to infer the strength and duration of the interaction between influenza and RSV. Based on our estimates, we further utilize our model to evaluate the potential conflicting effects of live attenuated influenza vaccines (LAIV) on RSV burden. We find evidence of a moderate to strong, negative, bidirectional interaction, such that infection with either virus yields 40-100% protection against infection with the other for one to five months. Assuming that LAIV reduces RSV susceptibility in a similar manner, we predict that the impact of such a vaccine at the population level would likely depend greatly on underlying viral circulation patterns. More broadly, we highlight the utility of mathematical models as a tool to characterize pathogen-pathogen interactions.

The study focuses on the effect of temperature and relative humidity on hospitalization for acute lower respiratory tract infections (LRTI) in children, respectively.

In this study, the Distributed Lag Nonlinear Model (DLNM) based on quasi-Poisson distribution was used to investigate the effect of temperature and relative humidity on LRTI hospitalization in children, and subgroup analyses were conducted to identify sensitive populations by gender and age.

A total of 43,951 children were hospitalized for LRTI from 1 January 2014 to 31 December 2019 in Lanzhou. The mean temperature during the study period was 11.34 °C and the mean relative humidity was 51.03%. With reference to the median temperature of 12.7 °C during the study period, both low (-4.1 °C) and high (25.43 °C) temperature had a detrimental effect on LRTI hospitalization, and the maximum effect was reached at lag0-10 and lag0-9, respectively, with RR values of 1.645 (95%CI: 1.533, 1.764) and 1.098 (95%CI: 1.018, 1.184). With a reference to the median relative humidity of 51.17% during the study period, both low relative humidity (26.71%) and high relative humidity (76.70%, P95) had a detrimental effect on LRTI hospitalization, and the maximum effect was reached at lag0-21 and lag21, respectively, with RR values of 1.235 (95% CI: 1.163, 1.311) and 1.044 (95% CI: 1.036, 1.051). The results of subgroup analyses showed that changes in meteorological factors had a stronger effect on Female and children aged 5-14 years.

The meteorological factors all have different degrees of influence on LRTI hospitalization in children. Girls and the children aged 5-14 years are more sensitive. Attention to these meteorological risks can inform targeted interventions.

Relative humidity (RH) is measured in vivaria with a broad range to accommodate seasonal fluctuations. It is assumed that measurements in the room (macroenvironment) reflect those in the cage (microenvironment). However, there is limited data comparing RH in the macroenvironment to the microenvironment and how the mice may be affected by variations in RH that fall within husbandry recommendations. This study aimed to compare RH in the macroenvironment to that of the microenvironment in various group sizes of laboratory mice; and examine how variation in microenvironmental RH impacts pup survival. Temperature and RH were measured using a temperature/humidity data logger attached to a solid top cage lid. The lid was rotated across N = 48 breeding trios and N = 33 same sex cages on a C57BL/6J background. Further, once a week, a single breeding trio was selected (N = 23) to compare RH readings to weekly rates of pup loss in a larger breeding colony. Across all cages, RH was higher in the microenvironment than the macroenvironment. RH was universally higher in the summer than in the winter, and increased with group size. For breeding cages, as microenvironmental RH increased, the proportion of pups lost each week decreased in a linear relationship. No threshold of decreased mortality could be identified. These data highlight RH as a potential extrinsic factor. While these patterns are correlational, they warrant further research focused on the causative role of RH on mouse welfare.

Climate change may have devastating effects on the pathogenesis of non-cystic fibrosis bronchiectasis in children since it affects the biological cycle of the respiratory pathogens and alters the human respiratory defense mechanisms. Bronchiectasis in children has been identified as an emerging global epidemic that has attracted the attention of the medical community over recent years. Pediatric pulmonologists should be aware of the consequences of climate change on children with bronchiectasis and plan strategies to ameliorate these effects.

We describe a novel biosafety aerosol chamber equipped with state-of-the-art instrumentation for bubble-bursting aerosol generation, size distribution measurement, and condensation-growth collection to minimize sampling artifacts when measuring virus infectivity in aerosol particles. Using this facility, we investigated the effect of relative humidity (RH) in very clean air without trace gases (except ∼400 ppm CO2) on the preservation of influenza A virus (IAV) infectivity in saline aerosol particles. We characterized infectivity in terms of 99%-inactivation time, t 99, a metric we consider most relevant to airborne virus transmission. The viruses remained infectious for a long time, namely t 99 > 5 h, if RH < 30% and the particles effloresced. Under intermediate conditions of humidity (40% < RH < 70%), the loss of infectivity was the most rapid (t 99 ≈ 15-20 min, and up to t 99 ≈ 35 min at 95% RH). This is more than an order of magnitude faster than suggested by many previous studies of aerosol-borne IAV, possibly due to the use of matrices containing organic molecules, such as proteins, with protective effects for the virus. We tested this hypothesis by adding sucrose to our aerosolization medium and, indeed, observed protection of IAV at intermediate RH (55%). Interestingly, the t 99 of our measurements are also systematically lower than those in 1-μL droplet measurements of organic-free saline solutions, which cannot be explained by particle size effects alone.

The detrimental effects of air pollution on the respiratory system are well documented. Previous research has established a correlation between air pollutant concentration and the frequency of outpatient visits for influenza-like illness. However, studies investigating the variations in infection among different influenza subtypes remain sparse. We aimed to determine the correlation between air pollutant levels and different influenza subtypes in Sichuan Province, China.

A generalized additive model and distributed lag nonlinear model were employed to assess the association between air pollutants and influenza subtypes, utilizing daily influenza data obtained from 30 hospitals across 21 cities in Sichuan Province. The analysis considered the temporal effects and meteorological factors. The study spanned from January 1, 2017, to December 31, 2019. To provide a more precise evaluation of the actual impact of air pollution on different subtypes of influenza, we also performed subgroup analyses based on factors such as gender, age, and geography within the population.

During the investigation, 17,462 specimens from Sichuan Province tested positive for influenza. Among these, 12,607 and 4855 were diagnosed with Flu A and B, respectively. The related risk of influenza A infection significantly increased following exposure to PM2.5 on Lag2 days (RR=1.008, 95 % confidence interval [CI]: 1.000-1.016), SO2 and CO on Lag1 days (RR=1.121, 95 % CI: 1.032-1.219; RR=1.151, 95 % CI: 1.030-1.289), and NO2 on Lag0 day (RR=1.089, 95 % CI: 1.035-1.145). PM10 and SO2 levels on Lag0 day, PM2.5 levels on Lag1 day, and CO levels on Lag6 day, with a reduced risk of influenza B (RR=0.987, 95 % CI: 0.976-0.997; RR=0.817, 95 % CI: 0.676-0.987; RR=0.979, 95 % CI: 0.970-0.989; RR=0.814, 95 % CI: 0.561-0.921).

The findings from the overall population and subgroup analyses indicated that the impact of air pollutant concentrations on influenza A and B is inconsistent, with influenza A demonstrating greater susceptibility to these pollutants. Minimizing the levels of SO2, CO, NO2, and PM2.5 can significantly decrease the likelihood of contracting influenza A. Analyzing the influence of environmental contaminants on different influenza subtypes can provide insights into seasonal influenza trends and guide the development of preventive and control strategies.

The composition of respiratory fluids influences the stability of viruses in exhaled aerosol particles and droplets, though the role of respiratory organics in modulating virus stability remains poorly understood. This study investigates the effect of organic compounds on the stability of influenza A virus (IAV) in deposited droplets. We compare the infectivity loss of IAV at different relative humidities (RHs) over the course of 1 h in 1-µL droplets consisting of phosphate-buffered saline (without organics), synthetic lung fluid, or nasal mucus (both containing organics). We show that IAV stability increases with increasing organic:salt ratios. Among the various organic species, proteins are identified as the most protective component, with smaller proteins stabilizing IAV more efficiently at the same mass concentration. Organics act by both increasing the efflorescence RH and shortening the drying period until efflorescence at a given RH. This research advances our mechanistic understanding of how organics stabilize exhaled viruses and thus influence their inactivation in respiratory droplets.

This study investigates how the composition of respiratory fluids affects the stability of viruses in exhaled droplets. Understanding virus stability in droplets is important as it impacts how viruses spread and how we can combat them. We focus on influenza A virus (IAV) and investigate how different organic compounds found in lung fluid and nasal mucus protect the virus from inactivation. We demonstrate that the ratio of organics to salt in the fluid is an indicator of IAV stability. Among organics, small proteins are particularly effective at protecting IAV. Their effect is in part explained by the proteins' influence on the crystallization of salts in the droplets, thereby shielding the viruses from prolonged exposure to harmful salt concentrations. Understanding these mechanisms helps us grasp how viruses sustain their infectivity over time in respiratory droplets, contributing to efforts in controlling infectious diseases.

Vitamin D is a steroid hormone that is naturally produced in the body or obtained through dietary sources, primarily under the influence of UVB radiation. This essential nutrient has a vital role in numerous physiological processes, encompassing immune function, cell growth, differentiation, insulin regulation, and cardiovascular well-being, along with its pivotal role in sustaining the delicate equilibrium of calcium and phosphate concentrations in the body. Moreover, vitamin D reinforces mucosal defense and bolsters the immune system through immunomodulation, making it a critical component of overall health. Numerous studies have unveiled the profound connection between vitamin D and the predisposition to respiratory tract infections, including well-known viruses such as influenza and the novel severe acute respiratory syndrome coronavirus 2. Vitamin D deficiency has been consistently linked to increased severity of coronavirus disease 2019 (COVID-19) and a heightened risk of mortality among afflicted individuals. Retrospective observational studies have further substantiated these findings, indicating that levels of vitamin D are linked with both the occurrence and severity of COVID-19 cases. Vitamin D has its influence on viral infections through a multitude of mechanisms, such as promoting the release of antimicrobial peptides and fine-tuning the responses of the immune system. Additionally, vitamin D is intertwined with the intricate network of the renin-angiotensin system, suggesting a potential impact on the development of complications related to COVID-19. While further clinical trials and extensive research are warranted, the existing body of evidence strongly hints at the possible use of vitamin D as a valuable tool in the prophylaxis and management of COVID-19 and other viral infectious diseases.

The prevalence of airborne pathogens in indoor environments presents significant health risks due to prolonged human occupancy. This review addresses diverse air purification systems to combat airborne pathogens and the factors influencing their efficacy. Indoor aerosols, including bioaerosols, harbor biological contaminants from respiratory emissions, highlighting the need for efficient air disinfection strategies. The COVID-19 pandemic has emphasized the dangers of airborne transmission, highlighting the importance of comprehending how pathogens spread indoors. Various pathogens, from viruses like SARS-CoV-2 to bacteria like Mycobacterium (My) tuberculosis, exploit unique respiratory microenvironments for transmission, necessitating targeted air purification solutions. Air disinfection methods encompass strategies to reduce aerosol concentration and inactivate viable bioaerosols. Techniques like ultraviolet germicidal irradiation (UVGI), photocatalytic oxidation (PCO), filters, and unipolar ion emission are explored for their specific roles in mitigating airborne pathogens. This review examines air purification systems, detailing their operational principles, advantages, and limitations. Moreover, it elucidates key factors influencing system performance. In conclusion, this review aims to provide practical knowledge to professionals involved in indoor air quality management, enabling informed decisions for deploying efficient air purification strategies to safeguard public health in indoor environments.

Studies have reported the adverse effects of cold events on influenza. However, the role of critical factors, such as characteristics of cold spells, and regional variations remain unresolved.

We aimed to systematically evaluate the association between cold spells and influenza incidence in mainland China.

This time series analysis used surveillance data of daily influenza from 325 sites in China in the 2014-2019 period. A total of 15 definitions of cold spells were adopted based on combinations of temperature thresholds and days of duration. A distributed lag linear model was used to estimate the short-term effects of cold spells on influenza incidence during the cool seasons (November to March), and we further explored the potential impact of cold spell characteristics (ie, intensity, duration, and timing during the season) on the estimated associations. Meta-regressions were used to evaluate the modification effect of city-level socioeconomic indicators.

The overall effect of cold spells on influenza incidence increased with the temperature threshold used to define cold spells, whereas the added effects were generally small and not statistically significant. The relative risk of influenza-associated with cold spells was 3.35 (95% CI 2.89-3.88), and the estimated effects were stronger during the middle period of cool seasons. The health effects of cold spells varied geographically and residents in Jiangnan region were vulnerable groups (relative risk 7.36, 95% CI 5.44-9.95). The overall effects of cold spells were positively correlated with the urban population density, population size, gross domestic product per capita, and urbanization rate, indicating a sterner response to cold spells in metropolises.

Cold spells create a substantial health burden on seasonal influenza in China. Findings on regional and socioeconomic differences in the health effects of cold spells on seasonal influenza may be useful in formulating region-specific public health policies to address the hazardous effects of cold spells.

This experimental study investigates the influence of indoor plants on three aspects of air quality in office spaces: relative humidity, indoor air temperature, and carbon dioxide concentration. Employing a Latin square design, we rotated three different treatments across three offices over six time periods. These treatments included a control (no plants), a low-volume treatment (five plants), and a high-volume treatment (eighteen plants) of Nephrolepis exaltata (Boston fern). Air quality parameters were continuously monitored at five-minute intervals using Trace Gas Analyzers. Generalised linear mixed modelling (GLMM) was employed to examine the effect of each treatment on relative humidity, indoor air temperature and CO2 concentration. We observed a significant positive correlation between the number of indoor plants and relative humidity levels. In offices without any plants, the median relative humidity was 29.1%. This increased to 38.9% in offices with 5 plants and further to 49.2% in offices with 18 plants. However, we did not find significant associations between the number of indoor plants and indoor air temperature or corrected CO2 concentration. Our research provides support for the use of indoor plants to increase relative humidity, which can have health benefits in dry climates, but does not provide support for using indoor plants to regulate indoor air temperatures or CO2 concentration in office environments.

The microbiota is a key determinant of the physiology and immunity of animal hosts. The factors governing the transmissibility of viruses between susceptible hosts are incompletely understood. Bacteria serve as food for Caenorhabditis elegans and represent an integral part of the natural environment of C. elegans. We determined the effects of bacteria isolated with C. elegans from its natural environment on the transmission of Orsay virus in C. elegans using quantitative virus transmission and host susceptibility assays. We observed that Ochrobactrum species promoted Orsay virus transmission, whereas Pseudomonas lurida MYb11 attenuated virus transmission relative to the standard laboratory bacterial food Escherichia coli OP50. We found that pathogenic Pseudomonas aeruginosa strains PA01 and PA14 further attenuated virus transmission. We determined that the amount of Orsay virus required to infect 50% of a C. elegans population on P. lurida MYb11 compared with Ochrobactrum vermis MYb71 was dramatically increased, over three orders of magnitude. Host susceptibility was attenuated even further in the presence of P. aeruginosa PA14. Genetic analysis of the determinants of P. aeruginosa required for attenuation of C. elegans susceptibility to Orsay virus infection revealed a role for regulators of quorum sensing. Our data suggest that distinct constituents of the C. elegans microbiota and potential pathogens can have widely divergent effects on Orsay virus transmission, such that associated bacteria can effectively determine host susceptibility versus resistance to viral infection. Our study provides quantitative evidence for a critical role for tripartite host-virus-bacteria interactions in determining the transmissibility of viruses among susceptible hosts.

Temperature affects influenza transmission; however, currently, limited evidence exists about its effect in China at the national and city levels as well as how temperature can be integrated into influenza interventions.

Meteorological, pollutant, and influenza data from 201 cities in mainland China between 2013 and 2018 were analyzed at both the city and national levels to investigate the relationship between temperature and influenza prevalence. We examined the impact of temperature on the time-varying reproduction number (Rt) using generalized additive quasi-Poisson regression models combined with the distributed lag nonlinear model. Threshold temperatures were determined for seven regions based on the early warning threshold of serious influenza outbreaks, set at Rt = 1.2. A multivariate random-effects meta-analysis was employed to assess region-specific associations. The excess risk (ER) index was defined to investigate the correlation between Rt and temperature, modified based on seasonal and regional characteristics.

At the national level and in the central, northern, northwestern, and southern regions, temperature was found to be negatively correlated with relative risk, whereas the shapes of the data curves for the eastern, southwestern, and northeastern regions were not well defined. Low temperatures had an observable effect on influenza prevalence; however, the effects of high temperatures were not obvious. At an Rt of 1.2, the threshold temperatures for reaching a warning for serious influenza outbreaks were - 24.3 °C in the northeastern region, 16.6 °C in the northwestern region, and between 1℃ and 10 °C in other regions.

The study findings revealed that temperature had a varying effect on influenza transmission trends (Rt) across different regions in China. By identifying region-specific temperature thresholds at Rt = 1.2, more effective early warning systems for influenza outbreaks could be tailored. These findings emphasize the significance of the region-specific adaptation of influenza prevention and control measures.

The role of meteorological factors, such as rainfall or temperature, as key players in the transmission and survival of infectious agents is poorly understood. The aim of this study was to compare meteorological surveillance data with epidemiological surveillance data in Belgium and to investigate the association between intense weather events and the occurrence of infectious diseases. Meteorological data were aggregated per Belgian province to obtain weekly average temperatures and rainfall per province and categorized according to the distribution of the variables. Epidemiological data included weekly cases of reported pathogens responsible for gastroenteritis, respiratory, vector-borne and invasive infections normalized per 100 000 population. The association between extreme weather events and infectious events was determined by comparing the mean weekly incidence of the considered infectious diseases after each weather event that occurred after a given number of weeks. Very low temperatures were associated with higher incidences of influenza and parainfluenza viruses, Mycoplasma pneumoniae, rotavirus and invasive Streptococcus pneumoniae and Streptococcus pyogenes infections, whereas very high temperatures were associated with higher incidences of Escherichia coli, Salmonella spp., Shigella spp., parasitic gastroenteritis and Borrelia burgdorferi infections. Very heavy rainfall was associated with a higher incidence of respiratory syncytial virus, whereas very low rainfall was associated with a lower incidence of adenovirus gastroenteritis. This work highlights not only the relationship between temperature or rainfall and infectious diseases but also the most extreme weather events that have an individual influence on their incidence. These findings could be used to develop adaptation and mitigation strategies.

The prevalence of seasonal viral respiratory infections in temperate climates is relatively stable, but individual viruses vary. This phenomenon is not explained via the conventional view of influenza seasonality which is still incomplete. The viral-flow theory, an outsider theory about the seasonality of influenza, where insects buffer viruses, is able to explain the stable prevalence of viral respiratory infections. Alternative hypotheses to explain this phenomenon are discussed.

There is growing evidence that weather alters SARS-CoV-2 transmission, but it remains unclear what drives the phenomenon. One prevailing hypothesis is that people spend more time indoors in cooler weather, leading to increased spread of SARS-CoV-2 related to time spent in confined spaces and close contact with others. However, the evidence in support of that hypothesis is limited and, at times, conflicting. We use a mediation framework, and combine daily weather, COVID-19 hospital surveillance, cellphone-based mobility data and building footprints to estimate the relationship between daily indoor and outdoor weather conditions, mobility, and COVID-19 hospitalizations. We quantify the direct health impacts of weather on COVID-19 hospitalizations and the indirect effects of weather via time spent indoors away-from-home on COVID-19 hospitalizations within five Colorado counties between March 4th 2020 and January 31st 2021. We also evaluated the evidence for seasonal effect modification by comparing the results of all-season (using season as a covariate) to season-stratified models. Four weather conditions were associated with both time spent indoors away-from-home and 12-day lagged COVID-19 hospital admissions in one or more season: high minimum temperature (all-season), low maximum temperature (spring), low minimum absolute humidity (winter), and high solar radiation (all-season & winter). In our mediation analyses, we found evidence that changes in 12-day lagged hospital admissions were primarily via the direct effects of weather conditions, rather than via indirect effects by which weather changes time spent indoors away-from-home. Our findings do not support the hypothesis that weather impacted SARS-CoV-2 transmission via changes in mobility patterns during the first year of the pandemic. Rather, weather appears to have impacted SARS-CoV-2 transmission primarily via mechanisms other than human movement. We recommend further analysis of this phenomenon to determine whether these findings generalize to current SARS-CoV-2 transmission dynamics, as well as other seasonal respiratory pathogens.

As an acute respiratory disease, scarlet fever has great harm to public health. Some evidence indicates that the time distribution pattern of heavy PM2.5 pollution occurrence may have an impact on health risks. This study aims to reveal the relation between scaling features in high-concentrations PM2.5 (HC-PM2.5) evolution and scarlet fever incidence (SFI). Based on the data of Hong Kong from 2012 to 2019, fractal box-counting dimension (D) is introduced to capture the scaling features of HC-PM2.5. It has been found that index D can quantify the time distribution of HC-PM2.5, and lower D values indicate more cluster distribution of HC-PM2.5. Moreover, scale-invariance in HC-PM2.5 at different time scales has been discovered, which indicates that HC-PM2.5 occurrence is not random but follows a typical power-law distribution. Next, the exposure-response relationship between SFI and scale-invariance in HC-PM2.5 is explored by Distributed lag non-linear model, in conjunction with meteorological factors. It has been discovered that scale-invariance in HC-PM2.5 has a nonlinear effect on SFI. Low and moderate D values of HC-PM2.5 are identified as risk factors for SFI at small time-scale. Moreover, relative risk shows a decreasing trend with the increase of exposure time. These results suggest that exposure to short-term clustered HC-PM2.5 makes individual more prone to SFI than exposure to long-term uniform HC-PM2.5. This means that individuals in slightly-polluted regions may face a greater risk of SFI, once the PM2.5 concentration keeps rising. In the future, it is expected that the relative risk of scarlet fever for a specific region can be estimated based on the quantitative analysis of scaling features in high-concentrations PM2.5 evolution.

Equine influenza (EI) is a severe infectious disease that causes huge economic losses to the horse industry. Spatial epidemiology technology can explore the spatiotemporal distribution characteristics and occurrence risks of infectious diseases, it has played an important role in the prevention and control of major infectious diseases in humans and animals. For the first time, this study conducted a systematic analysis of the spatiotemporal distribution of EI using SaTScan software and investigated the important environmental variables and suitable areas for EI occurrence using the Maxent model. A total of 517 occurrences of EI from 2005 to 2022 were evaluated, and 14 significant spatiotemporal clusters were identified. Furthermore, a Maxent model was successfully established with high prediction accuracy (AUC = 0.920 ± 0.008). The results indicated that annual average ultraviolet radiation, horse density, and precipitation of the coldest quarter were the three most important environmental variables affecting EI occurrence. The suitable areas for EI occurrence are widely distributed across all continents, especially in Asia (India, Mongolia, and China) and the Americas (Brazil, Uruguay, USA, and Mexico). In the future, these suitable areas will expand and move eastward. The largest expansion is predicted under SSP126 scenarios, while the opposite trend will be observed under SSP585 scenarios. This study presents the spatial epidemiological characteristics of EI for the first time. The results could provide valuable scientific insights that can effectively inform prevention and control strategies in regions at risk of EI worldwide.

The inactivation of viruses in aerosol particles (aerosols) and droplets depends on many factors, but the precise mechanisms of inactivation are not known. The system involves complex physical and biochemical interactions. We reviewed the literature to establish current knowledge about these mechanisms and identify knowledge gaps. We identified 168 relevant papers and grouped results by the following factors: virus type and structure, aerosol or droplet size, temperature, relative humidity (RH) and evaporation, chemical composition of the aerosol or droplet, pH and atmospheric composition. These factors influence the dynamic microenvironment surrounding a virion and thus may affect its inactivation. Results indicate that viruses experience biphasic decay as the carrier aerosols or droplets undergo evaporation and equilibrate with the surrounding air, and their final physical state (liquid, semi-solid or solid) depends on RH. Virus stability, RH and temperature are interrelated, but the effects of RH are multifaceted and still not completely understood. Studies on the impact of pH and atmospheric composition on virus stability have raised new questions that require further exploration. The frequent practice of studying virus inactivation in large droplets and culture media may limit our understanding of inactivation mechanisms that are relevant for transmission, so we encourage the use of particles of physiologically relevant size and composition in future research.

We study the predetermined characteristics of countries in addition to their government non-pharmaceutical interventions (NPIs) to shed light on the correlates of the variation in COVID-19 infection outcomes across countries.

We conduct a systematic investigation of the validity of government responses in 84 countries by gradually adding the predetermined cultural, natural and socioeconomic factors of each country using a fixed-effect model and daily panel data. A relative importance analysis is conducted to isolate the contribution of each variable to the R2 of the model.

Government NPIs are effective in containing the virus spread and explain approximately 9% of the variations in the pandemic outcomes. COVID-19 is more prevalent in countries that are more individual-oriented or with a higher gross domestic product (GDP) per capita, while a country's government expenditure on health as a proportion of GDP and median age are negatively associated with the infection outcome. The SARS-CoV-2 lifecycle and the impacts of other unobserved factors together explain more than half of the variation in the prevalence of COVID-19 across countries. The degree of individualism explains 9.30% of the variation, and the explanatory power of the other socioeconomic factors is less than 4% each.

The COVID-19 infection outcomes are correlated with multivariate factors, ranging from state NPIs, culture-influenced human behaviours, geographical conditions and socioeconomic conditions. As expected, the stronger or faster are the government responses, the lower is the level of infections. In the meantime, many other factors underpin a major part of the variation in the control of COVID-19. As such, from a scientific perspective, it is important that country-specific conditions are taken into account when evaluating the impact of NPIs in order to conduct more cost-effective policy interventions.

The highly pathogenic Avian Influenza virus (HPAIV) H5N1 has caused a global outbreak affecting both wild and domestic animals, predominantly avian species. To date, cases of the HPAIV H5 Clade 2.3.4.4b in penguins have exclusively been reported in African Penguins. In Chile, the virus was confirmed in pelicans in December 2022 and subsequently spread across the country, affecting several species, including Humboldt penguins. This study aims to provide an overview of the incidents involving stranded and deceased Humboldt penguins and establish a connection between these events and HPAIV H5N1. Historical data about strandings between 2009 and 2023 was collected, and samples from suspected cases in 2023 were obtained to confirm the presence of HPAIV H5N1. Between January and August 2023, 2,788 cases of stranded and deceased penguins were recorded. Out of these, a total of 2,712 penguins deceased, evidencing a significative increase in mortality starting in early 2023 coinciding with the introduction and spreading of HPAIV H5N1 in the country. Thirty-seven events were categorized as mass mortality events, with the number of deceased penguins varying from 11 to 98. Most cases (97 %) were observed in the North of Chile. One hundred and eighty-one specimens were subjected to HPAIV diagnosis, four of which tested positive for HPAIV H5N1. Spatial analysis validates the correlation between mass mortality events and outbreaks of HPAIV in Chile. However, the limited rate of HPAIV H5N1 detection, which can be attributed to the type and quality of the samples, requiring further exploration.

In the last three years, COVID-19 has caused significant harm to both human health and economic stability. Analyzing the causes and mechanisms of COVID-19 has significant theoretical and practical implications for its prevention and mitigation. The role of meteorological factors in the transmission of COVID-19 is crucial, yet their relationship remains a subject of intense debate.

To mitigate the issues arising from short time series, large study units, unrepresentative data and linear research methods in previous studies, this study used counties or districts with populations exceeding 100,000 or 500,000 as the study unit. The commencement of local outbreaks was determined by exceeding 100 cumulative confirmed cases. Pearson correlation analysis, generalized additive model (GAM) and distributed lag nonlinear model (DLNM) were used to analyze the relationship and lag effect between the daily new cases of COVID-19 and meteorological factors (temperature, relative humidity, solar radiation, surface pressure, precipitation, wind speed) across 440 counties or districts in seven countries of the Americas, spanning from January 1, 2020, to December 31, 2021.

The linear correlations between daily new cases and meteorological indicators such as air temperature, relative humidity and solar radiation were not significant. However, the non-linear correlations were significant. The turning points in the relationship for temperature, relative humidity and solar radiation were 5 °C and 23 °C, 74 % and 750 kJ/m2, respectively.

The influence of meteorological factors on COVID-19 is non-linear. There are two thresholds in the relationship with temperature: 5 °C and 23 °C. Below 5 °C and above 23 °C, there is a positive correlation, while between 5 °C and 23 °C, the correlation is negative. Relative humidity and solar radiation show negative correlations, but there is a change in slope at about 74 % and 750 kJ/m2, respectively.

Outbreaks of highly pathogenic H5N1 clade 2.3.4.4b viruses in farmed mink and seals combined with isolated human infections suggest these viruses pose a pandemic threat. To assess this threat, using the ferret model, we show an H5N1 isolate derived from mink transmits by direct contact to 75% of exposed ferrets and, in airborne transmission studies, the virus transmits to 37.5% of contacts. Sequence analyses show no mutations were associated with transmission. The H5N1 virus also has a low infectious dose and remains virulent at low doses. This isolate carries the adaptive mutation, PB2 T271A, and reversing this mutation reduces mortality and airborne transmission. This is the first report of a H5N1 clade 2.3.4.4b virus exhibiting direct contact and airborne transmissibility in ferrets. These data indicate heightened pandemic potential of the panzootic H5N1 viruses and emphasize the need for continued efforts to control outbreaks and monitor viral evolution.

We estimated the hourly probability of airborne severe acute respiratory coronavirus 2 (SARS-CoV-2) transmission and further the estimated number of persons at transmission risk in a day care centre by calculating the inhaled dose for airborne pathogens based on their concentration, exposure time and activity. Information about the occupancy and activity of the rooms was collected from day care centre personnel and building characteristics were obtained from the design values. The generation rate of pathogens was calculated as a product of viral load of the respiratory fluids and the emission of the exhaled airborne particles, considering the prevalence of the disease and the activity of the individuals. A well-mixed model was used in the estimation of the concentration of pathogens in the air. The Wells-Riley model was used for infection probability. The approach presented in this study was utilised in the identification of hot spots and critical events in the day care centre. Large variation in the infection probabilities and estimated number of persons at transmission risk was observed when modelling a normal day at the centre. The estimated hourly infection probabilities between the worst hour in the worst room and the best hour in the best room varied in the ratio of 100:1. Similarly, the number of persons at transmission risk between the worst and best cases varied in the ratio 1000:1. Although there are uncertainties in the input values affecting the absolute risk estimates the model proved to be useful in ranking and identifying the hot spots and events in the building and implementing effective control measures.

Respiratory disease transmission in indoor environments presents persistent challenges for health authorities, as exemplified by the recent COVID-19 pandemic. This underscores the urgent necessity to investigate the dynamics of viral infection transmission within indoor environments. This systematic review delves into the methodologies of respiratory infection transmission in indoor settings and explores how the quality of indoor air (IAQ) can be controlled to alleviate this risk while considering the imperative of sustainability. Among the 2722 articles reviewed, 178 were retained based on their focus on respiratory viral infection transmission and IAQ. Fifty eight articles delved into SARS-CoV-2 transmission, 21 papers evaluated IAQ in contexts of other pandemics, 53 papers assessed IAQ during the SARS-CoV-2 pandemic, and 46 papers examined control strategies to mitigate infectious transmission. Furthermore, of the 46 papers investigating control strategies, only nine considered energy consumption. These findings highlight clear gaps in current research, such as analyzing indoor air and surface samples for specific indoor environments, oversight of indoor and outdoor parameters (e.g., temperature, relative humidity (RH), and building orientation), neglect of occupancy schedules, and the absence of considerations for energy consumption while enhancing IAQ. This study distinctly identifies the indoor environmental conditions conducive to the thriving of each respiratory virus, offering IAQ trade-offs to mitigate the risk of dominant viruses at any given time. This study argues that future research should involve digital twins in conjunction with machine learning (ML) techniques. This approach aims to enhance IAQ by analyzing the transmission patterns of various respiratory viruses while considering energy consumption.

Influenza affects a considerable proportion of the global population each year, and meteorological conditions may have a significant impact on its transmission. In this study, we aimed to develop a prediction model for the number of influenza patients at the national level using satellite images and provide a basis for predicting influenza through satellite image data.

We developed an influenza incidence prediction model using satellite images and influenza patient data.

We collected satellite images and daily influenza patient data from July 2014 to June 2019 and developed a convolutional long short-term memory (LSTM)-LSTM neural network model. The model with the lowest average of mean absolute error (MAE) was selected.

The final model showed a high correlation between the predicted and actual number of influenza patients, with an average MAE of 5.9010 per million population. The model performed best with a 2-week time sequence.

We developed a national-level prediction model using satellite images to predict influenza incidence. The model offers the advantage of nationwide analysis. These results may reduce the burden of influenza by enabling timely public health interventions.