Discrepancies in fine particulate matter (PM2.5)-related influenza-like illness (ILI) risk have been widely observed in different studies in China, where the individual effect of PM2.5 constituents might be one of the important reasons. However, the associations between PM2.5 constituents and ILI risk in China have yet to be understood. We collected and aggregated weekly ILI cases in 289 Chinese cities during 2006-2019, and 47.8 million ILI cases were finally included in this study. Quasi-Poisson regression models and a random-effect meta-analysis were applied to estimate the impacts of PM2.5 and its constituents on ILI risk. Stratification analyses were also conducted by region, age group, season, and temperature and humidity quartiles. With per inter-quartile range increase in black carbon, ammonium, sulfate, PM2.5, nitrate and organic matter with a cumulative lag of 0-1 week, the overall ILI incidence would increase by 2.55 % (95 % CI: 1.71, 3.40), 2.32 % (1.33, 3.32), 2.19 % (1.29, 3.10), 2.19 % (1.25, 3.13), 2.15 % (1.08, 3.22) and 2.02 % (1.19, 2.85), respectively. The impacts tended to be much stronger in young- and middle-aged population, in North and East China, in winter, and in colder and drier conditions. PM2.5 and its major constituents all have significantly additive effects on ILI incidence. Specific preventive measures against individual constituent should be implemented for improving public health.

Temperature extremes are associated with a variety of negative health outcomes, but since people living in developed areas of the world spend most of their time indoors, outdoor temperatures are a poor substitute for personal exposure assessment. And the importance of accurate indoor temperature measurement has only become more apparent alongside the growing impact of climate change on the frequency and intensity of temperature extremes on public health. The development and implementation of low-cost sensors have improved economic and practical feasibility of in-home exposure assessment for temperature and a variety of indoor contaminants. One example is the PurpleAir particulate matter sensor. However, onboard electronics likely bias measured temperature (T) and relative humidity (RH) values in such devices. The objectives of this investigation were to (1) characterize bias and error of T and RH estimates by comparing 30,936 hourly mean values to a dedicated, calibrated temperature and RH sensor co-located at 115 homes in Baltimore, MD, (2) develop calibration equations to accurately approximate the reference values, (3) validate the performance of these equations, and (4) validate the transportability of these calibration equations using 7697 pairs of hourly mean temperature and RH values from 22 homes in western Maryland. The PurpleAir sensors measured higher temperatures and lower RH than indoor reference measurements. Calibration schemes using a bias correction and multiple linear regression were considered. Calibration of PurpleAir temperature in °C (TPA) may be performed by Tindoor = TPA - 3.95 °C (R2 = 0.80), and calibration of PurpleAir RH measured from 0-100% (RHPA) may be performed by RHindoor =  - 0.29 + 1.39∙RHPA (R2 = 0.92). Calibration-corrected temperature and RH demonstrated low bias and error values, and these results were consistent in the withheld datasets.

The cross-species transmission of influenza viruses represents a critical link in the pandemic of zoonotic diseases. This mechanism involves multi-level interactions, including viral genetic adaptability, host-receptor compatibility, and ecological drivers. Recent studies have highlighted the essential role of mutations in hemagglutinin and neuraminidase in overcoming host barriers, while elucidating the differences in the distribution of host sialic acid receptors. Furthermore, the "mixer" function of intermediate hosts, such as pigs, plays a significant role in viral redistribution. Advances in high-throughput sequencing and structural biology technologies have gradually resolved key molecular markers and host restriction factors associated with these viruses. However, challenges remain in understanding the dynamic evolutionary patterns of virus-host interaction networks, developing real-time early warning capabilities for cross-species transmission, and formulating broad-spectrum prevention and control strategies. Moving forward, it is essential to integrate multidisciplinary approaches to establish a multi-level defense system, leveraging the 'One Health' monitoring network, artificial intelligence prediction models, and new vaccine research and development to address the ongoing threat of cross-species transmission of influenza viruses. This paper systematically reviews the research progress and discusses bottlenecks in this field, providing a theoretical foundation for optimizing future prevention and control strategies.

During the coronavirus disease (COVID-19) pandemic, researchers attempted to estimate the number of averted and avertible outcomes due to vaccination campaigns to quantify public health impact. However, the estimands used in these analyses have not been previously formalized. It is also unclear how these analyses relate to the broader framework of direct, indirect, total, and overall causal effects under interference. Here, using potential outcome notation, we adjust the direct and overall effects to accommodate analyses of averted and avertible outcomes. We use this framework to interrogate the commonly held assumption that vaccine-averted outcomes via direct impact among vaccinated individuals (or vaccine-avertible outcomes via direct impact among unvaccinated individuals) is a lower bound on vaccine-averted (or -avertible) outcomes overall. To do so, we describe a susceptible-infected-recovered-death model stratified by vaccination status. When vaccine efficacies wane, the lower bound fails for vaccine-avertible outcomes. When transmission or fatality parameters increase over time, the lower bound fails for both vaccine-averted and -avertible outcomes. Only in the simplest scenario where vaccine efficacies, transmission, and fatality parameters are constant over time, outcomes averted via direct impact among vaccinated individuals (or outcomes avertible via direct impact among unvaccinated individuals) is a lower bound on overall impact. In conclusion, the lower bound can fail under common violations to assumptions on time-invariant vaccine efficacy, pathogen properties, or behavioral parameters. In real data analyses, estimating what seems like a lower bound on overall impact through estimating direct impact may be inadvisable without examining the directions of indirect effects.

Ambient humidity, temperature, and ozone influence the viability of airborne viruses, but their synergistic effects are poorly understood, particularly regarding ozone with humidity/temperature changes. Therefore, we examined the inactivation of airborne influenza viruses and coronaviruses under combinations of low ambient ozone concentrations, relative humidity (RH) levels, and temperatures typical of daily life. Viral fluid was atomized in a closed chamber conditioned with different combinations of these factors. The atomized aerosol particles containing the virus were exposed to ambient air and then sampled for titration. Active virus levels in ambient air at 50%-85% RH with 15, 35, and 55 ppb ozone significantly decreased compared with those in ambient air with 0 ppb ozone, whereas those in ambient air at < 40% RH decreased only slightly, even with 100 ppb ozone. Viral gene copy numbers, assayed via quantitative real-time polymerase chain reaction, remained similar across all conditions. Inactivation increased with higher temperatures, although not at 15°C. These findings suggest that low concentrations of ambient ozone, when combined with high humidity and temperature, effectively inactivate airborne viruses, potentially influencing viral transmission in real-world environments.

The COVID-19 pandemic in New York City (NYC) was characterized by marked disparities in disease burdens across neighborhoods. Accurate neighborhood-level forecasts are critical for planning more equitable resource allocation to reduce health inequalities; however, such spatially high-resolution forecasts remain scarce in operational use. In this study, we analyze aggregated foot traffic data derived from mobile devices to measure the connectivity among 42 NYC neighborhoods driven by various human activities such as dining, shopping, and entertainment. Using real-world time-varying contact patterns in different place categories, we develop a parsimonious behavior-driven epidemic model that incorporates population mixing, indoor crowdedness, dwell time, and seasonality of virus transmissibility. We fit this model to neighborhood-level COVID-19 case data in NYC and further couple this model with a data assimilation algorithm to generate short-term forecasts of neighborhood-level COVID-19 cases in 2020. We find differential contact patterns and connectivity between neighborhoods driven by different human activities. The behavior-driven model supports accurate modeling of neighborhood-level SARS-CoV-2 transmission throughout 2020. In the best-fitting model, we estimate that the force of infection (FOI) in indoor settings increases sublinearly with crowdedness and dwell time. Retrospective forecasting demonstrates that this behavior-driven model generates improved short-term forecasts in NYC neighborhoods compared to several baseline models. Our findings indicate that aggregated foot-traffic data for routine human activities can support neighborhood-level COVID-19 forecasts in NYC. This behavior-driven model may be adapted for use with other respiratory pathogens sharing similar transmission routes.

Influenza epidemics, a major contributor to global morbidity and mortality, are influenced by climate factors including absolute humidity and temperature. Climate change is expected to increase the frequency and severity of climate extremes, potentially impacting the duration and magnitude of future influenza epidemics. However, the extent of these projected effects on influenza outbreaks remains understudied. Here, we use an epidemiologic model adapted for temperate and tropical climates to explore how climate variability may affect seasonal influenza. Using climate anomalies derived from historical data, we found that simulated periods of anomalous climate conditions impacted both the projected influenza outbreak peak size and the total proportion infected, with the strongest effects observed when the anomaly was included just before the typical peak. Effects varied by climate: temperate regions showed a unimodal relationship, while tropical climates exhibited a nonlinear pattern. Our results emphasize that the intensity of weather extremes is key to understanding how climate change may affect influenza outbreaks, laying the groundwork for utilizing weather variability as a potential early warning for influenza activity.

Viral infectious diseases, characterized by their intricate nature and wide-ranging diversity, pose substantial challenges in the domain of data management. The vast volume of data generated by these diseases, spanning from the molecular mechanisms within cells to large-scale epidemiological patterns, has surpassed the capabilities of traditional analytical methods. In the era of artificial intelligence (AI) and big data, there is an urgent necessity for the optimization of these analytical methods to more effectively handle and utilize the information. Despite the rapid accumulation of data associated with viral infections, the lack of a comprehensive framework for integrating, selecting, and analyzing these datasets has left numerous researchers uncertain about which data to select, how to access it, and how to utilize it most effectively in their research.This review endeavors to fill these gaps by exploring the multifaceted nature of viral infectious diseases and summarizing relevant data across multiple levels, from the molecular details of pathogens to broad epidemiological trends. The scope extends from the micro-scale to the macro-scale, encompassing pathogens, hosts, and vectors. In addition to data summarization, this review thoroughly investigates various dataset sources. It also traces the historical evolution of data collection in the field of viral infectious diseases, highlighting the progress achieved over time. Simultaneously, it evaluates the current limitations that impede data utilization.Furthermore, we propose strategies to surmount these challenges, focusing on the development and application of advanced computational techniques, AI-driven models, and enhanced data integration practices. By providing a comprehensive synthesis of existing knowledge, this review is designed to guide future research and contribute to more informed approaches in the surveillance, prevention, and control of viral infectious diseases, particularly within the context of the expanding big-data landscape.

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.

Titrating the relative importance of endogenous and exogenous drivers for dynamical transitions in host-pathogen systems remains an important research frontier towards predicting future outbreaks and making public health decisions. In Japan, respiratory syncytial virus (RSV), a major childhood respiratory pathogen, displayed a sudden, dramatic shift in outbreak seasonality (from winter to fall) in 2016. This shift was not observed in any other countries. We use mathematical models to identify processes that could lead to this outcome. In line with previous analyses, we identify a robust quadratic relationship between mean specific humidity and transmission, with minimum transmission occurring at intermediate humidity. This drives semiannual patterns of seasonal transmission rates that peak in summer and winter. Under this transmission regime, a subtle increase in population-level susceptibility can cause a sudden shift in seasonality, where the degree of shift is primarily determined by the interval between the two peaks of seasonal transmission rate. We hypothesize that an increase in children attending childcare facilities may have contributed to the increase in susceptibility through increased contact rates with susceptible hosts. Our analysis underscores the power of studying infectious disease dynamics to titrate the roles of underlying drivers of dynamical transitions in ecology.

Studies have documented the impact of temperature on the incidence of hand, foot, and mouth disease (HFMD); however, no study has examined its impact on the transmissibility.

The longitudinal surveillance data of HFMD in Zhejiang Province during 2013-2019 were collected from National Notifiable Infectious Diseases Reporting Information System. The incidence of HFMD was represented by daily case counts, and the transmissibility was quantified as the instantaneous reproductive number ([Formula: see text]). The case time series design was applied to investigate the association between temperature and HFMD incidence at small-scale spatial patterns (i.e., townships). General additive model was further employed to analyze the effects of temperature and other driving factors on the transmissibility of HFMD. Separate models were also conducted for each city, along with seasonal and spatial stratified analysis.

We observed an inverted V-shaped association between temperature and HFMD incidence, with the highest cumulative relative risk (RR: 3.81, 95% CI: 3.75-3.86) at 28°C compared to the reference temperature. Notably, we discovered that HFMD transmissibility exhibited a similar but more pronounced sensitivity to temperature changes, peaking at a lower temperature of 19.69°C. City-specific and stratified results were aligned with the overall provincial pattern. Additionally, other significant driving factors of HFMD transmissibility included the depletion of susceptible individuals, school holidays, vaccination program, relative humidity, and the Normalized Difference Vegetation Index.

Nonlinear associations between temperature and HFMD incidence, as well as transmissibility, are observed. Other driving factors potentially contribute to changes in HFMD dynamic transmission. These findings underscore the importance of implementing targeted policies aimed at early intervention, particularly when HFMD transmissibility begins to reach its peak.

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.

Background/Objectives: This study aimed to evaluate the prevalence of viral agents identified by Multiplex PCR in acute respiratory viral infection (ARVI) patients at Edirne Sultan 1, Murat State Hospital, from April 2023 to April 2024, and to investigate the relationship between monthly average humidity and viral positivity rates. Methods: The study included 764 adult patients (aged 18 and older) diagnosed with influenza symptoms. Respiratory viral samples were collected and analyzed for COVID-19, influenza A and B, and RSV using Multiplex PCR, with results evaluated retrospectively. Continuous variables in the study were compared using a t-test, and categorical variables were compared with a chi-square test. A logistic regression analysis was performed for the analysis of COVID-19. In this analysis, PCR positivity was the dependent variable, while age, gender, and humidity level served as independent variables. Results: COVID-19 PCR positivity was detected in 142 patients (18.6%), with INF-A (influenza A) in 13 (3.7%), INF-B (influenza B) in 15 (4.2%), and RSV in 2 (0.6%). Higher humidity (over 60%) was associated with reduced viral PCR positivity rates for COVID-19 and influenza B, while low (up to 40%)/normal (40-60%) humidity correlated with positivity rate (p < 0.05 for both). Logistic regression analysis indicated that high humidity levels offer protection against COVID-19 (OR: 0.356; 95% CI: 0.245-0.518). Conclusions: Our study provides essential epidemiological data by summarizing monthly virus distribution in Edirne.

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.

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.

Bangladesh is facing a formidable challenge in mitigating waterborne diseases risk exacerbated by climate change. However, a comprehensive understanding of the spatio-temporal dynamics of these diseases at the district level remains elusive. Therefore, this study aimed to fill this gap by investigating the spatio-temporal pattern and identifying the best tree-based ML models for determining the meteorological factors associated with waterborne diseases in Bangladesh.

This study used district-level reported cases of waterborne diseases (cholera, amoebiasis, typhoid and hepatitis A) obtained from the Bangladesh Bureau of Statistics (BBS) and meteorological data (temperature, relative humidity, wind speed, and precipitation) sourced from NASA for the period spanning 2017 to 2020. Exploratory spatial analysis, spatial regression and tree-based machine learning models were utilized to analyze the data.

From 2017 and 2020, Bangladesh reported 73, 606 cholera, 38, 472 typhoid, 2, 510 hepatitis A and 1, 643 amoebiasis disease cases. Among the waterborne diseases cholera showed higher incidence rates in Chapai-Nawabganj (456.23), Brahmanbaria (417.44), Faridpur (225.07), Nilphamari (188.62) and Pirojpur (171.62) districts. The spatial regression model identified mean temperature (β = 12.16, s.e: 3.91) as the significant risk factor of waterborne diseases. The optimal XGBoost model highlighted mean and minimum temperature, relative humidity and precipitation as determinants associated with waterborne diseases in Bangladesh from 2017 to 2020.

The findings from the study, incorporating the One Health perspective, provide insights for planning early warning, prevention, and control strategies to combat waterborne diseases in Bangladesh and similar endemic countries. Precautionary measures and intensified surveillance need to be implemented in certain high-risk districts for waterborne diseases across the country.

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.

In the last century, global pandemics have been primarily driven by respiratory infections, which consistently rank among the top 20 causes of death worldwide. The coronavirus disease 2019 (COVID-19) pandemic has underscored the intricate nature of managing multiple health crises simultaneously. In recent years, climate change has emerged as a major biosafety and population health challenge. Global warming and extreme weather events have intensified outbreaks of climate-sensitive infectious diseases, especially respiratory diseases. Influenza and COVID-19 have emerged as two of the most significant respiratory pandemics, each with unique epidemic characteristics and far-reaching consequences. Our comparative analysis reveals that while both diseases exhibit high transmission rates, COVID-19's longer incubation period and higher severity have led to more profound and prolonged socioeconomic disruptions than influenza. Both pandemics have highlighted the exacerbating effects of climate change, with extreme weather events intensifying the spread and impact of these diseases. The COVID-19 pandemic exposed vulnerabilities in global healthcare systems and economies on an unprecedented scale, outstripping the strain caused by influenza outbreaks. Importantly, the COVID-19 pandemic has not only reshaped global public health strategies but also significantly impacted the epidemiology of influenza. Despite these differences and associations, both diseases underscore the urgent need for robust pandemic preparedness and adaptable public health strategies. This review delineates the overlaps and distinctions between influenza and COVID-19, offering insights into future challenges and the critical steps needed to enhance healthcare system resilience and improve global responses to pandemics.

Humans experience many influenza infections over their lives, resulting in complex and varied immunological histories. Although experimental and quantitative analyses have improved our understanding of the immunological processes defining an individual's antibody repertoire, how these within-host processes are linked to population-level influenza epidemiology in humans remains unclear. Here, we used a multilevel mathematical model to jointly infer antibody dynamics and individual-level lifetime influenza A/H3N2 infection histories for 1,130 individuals in Guangzhou, China, using 67,683 haemagglutination inhibition (HI) assay measurements against 20 A/H3N2 strains from repeat serum samples collected between 2009 and 2015. These estimated infection histories allowed us to reconstruct historical seasonal influenza patterns in humans and to investigate how influenza incidence varies over time, space, and age in this population. We estimated median annual influenza infection rates to be approximately 19% from 1968 to 2015, but with substantial variation between years; 88% of individuals were estimated to have been infected at least once during the study period (2009 to 2015), and 20% were estimated to have 3 or more infections in that time. We inferred decreasing infection rates with increasing age, and found that annual attack rates were highly correlated across all locations, regardless of their distance, suggesting that age has a stronger impact than fine-scale spatial effects in determining an individual's antibody profile. Finally, we reconstructed each individual's expected antibody profile over their lifetime and inferred an age-stratified relationship between probability of infection and HI titre. Our analyses show how multi-strain serological panels provide rich information on long-term epidemiological trends, within-host processes, and immunity when analysed using appropriate inference methods, and adds to our understanding of the life course epidemiology of influenza A/H3N2.

Infectious disease is hypothesized to be one of the most important causes of morbidity and mortality in wild great apes. Specific socioecological factors have been shown to influence incidences of respiratory illness and disease prevalence in some primate populations. In this study, we evaluated potential predictors (including age, sex, group size, fruit availability, and rainfall) of respiratory illness across three western lowland gorilla groups in the Republic of Congo. A total of 19,319 observational health assessments were conducted during daily follows of habituated gorillas in the Goualougo and Djéké Triangles over a 4-year study period. We detected 1146 incidences of clinical respiratory signs, which indicated the timing of probable disease outbreaks within and between groups. Overall, we found that males were more likely to exhibit signs than females, and increasing age resulted in a higher likelihood of respiratory signs. Silverback males showed the highest average monthly prevalence of coughs and sneezes (Goualougo: silverback Loya, 9.35 signs/month; Djéké: silverback Buka, 2.65 signs/month; silverback Kingo,1.88 signs/month) in each of their groups. Periods of low fruit availability were associated with an increased likelihood of respiratory signs. The global pandemic has increased awareness about the importance of continuous monitoring and preparedness for infectious disease outbreaks, which are also known to threaten wild ape populations. In addition to the strict implementation of disease prevention protocols at field sites focused on great apes, there is a need for heightened vigilance and systematic monitoring across sites to protect both wildlife and human populations.

Seasonal influenza causes significant annual morbidity and mortality worldwide. In France, it is estimated that, on average, 2 million individuals consult their GP for influenza-like-illness (ILI) every year. Traditionally, mathematical models used for epidemic forecasting can either include parameters capturing the infection process (mechanistic or compartmental models) or rely on time series analysis approaches that do not make mechanistic assumptions (statistical or phenomenological models). While the latter make extensive use of past epidemic data, mechanistic models are usually independently initialized in each season. As a result, forecasts from such models can contain trajectories that are vastly different from past epidemics. We developed a mechanistic model that takes into account epidemic data from training seasons when producing forecasts. The parameters of the model are estimated via a first particle filter running on the observed data. A second particle filter is then used to produce forecasts compatible with epidemic trajectories from the training set. The model was calibrated and tested on 35 years' worth of surveillance data from the French Sentinelles Network, representing the weekly number of patients consulting for ILI over the period 1985-2019. Our results show that the new method improves upon standard mechanistic approaches. In particular, when retrospectively tested on the available data, our model provides increased accuracy for short-term forecasts (from one to four weeks into the future) and peak timing and intensity. Our new approach for epidemic forecasting allows the integration of key strengths of the statistical approach into the mechanistic modelling framework and represents an attempt to provide accurate forecasts by making full use of the rich surveillance dataset collected in France since 1985.

Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity.

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.

Influenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions (NPIs) have introduced uncertainty to the spread of influenza. However, comparative studies on the performance of innovative models and approaches used for influenza prediction are limited. Therefore, this study aimed to predict the trend of influenza-like illness (ILI) in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance.

The generalized additive model (GAM), deep learning hybrid model based on Gate Recurrent Unit (GRU), and autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model were established to predict the trends of ILI 1-, 2-, 3-, and 4-week-ahead in Beijing, Tianjin, Shanxi, Hubei, Chongqing, Guangdong, Hainan, and the Hong Kong Special Administrative Region in China, based on sentinel surveillance data from 2011 to 2019. Three relevant metrics, namely, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R squared, were calculated to evaluate and compare the goodness of fit and robustness of the three models.

Considering the MAPE, RMSE, and R squared values, the ARMA-GARCH model performed best, while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China. Additionally, the models' predictive performance declined as the weeks ahead increased. Furthermore, blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting.

Our study suggested that the ARMA-GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model. Therefore, in the future, the ARMA-GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones, thereby contributing to influenza control and prevention efforts.

Acute respiratory infections are a leading cause of morbidity and mortality in children. However, studies on the prevalence of respiratory viruses among children with acute respiratory infections in Kunming, China, are lacking. Therefore, we aimed to investigate the epidemiological characteristics of respiratory pathogens among children with acute respiratory infections in Kunming during the coronavirus disease 2019 pandemic.

Nasopharyngeal swab samples were collected from 4956 children with acute respiratory infections at Yunnan Provincial First People's Hospital between January 2020 and December 2022, patients with COVID-19 were excluded from the study. Multiplex reverse transcription polymerase chain reaction was used to detect respiratory pathogens.

The frequency of respiratory pathogens among children was significantly lower in 2020 than in 2021 and 2022. The following pathogens had the highest prevalence rates (in descending order) from 2020 to 2022: HRV > RSV > PIV > ADV > MP; HRV > RSV > HADV > PIV > MP and HRV > Mp > HADV > H3N2 > HMPV. The overall frequency of respiratory pathogens exhibited an inverted U-shape with increasing age among the children. Human bocavirus, human parainfluenza virus, and human respiratory syncytial virus were the dominant respiratory viruses in children aged ≤ 3 years, whereas Mycoplasma pneumoniae was the dominant respiratory pathogen in children aged > 3 years. HRV has the highest prevalence and is the main pathogen of mixed infection. The prevalence of the influenza A virus has decreased significantly, whereas HRSV and Mp are found to be seasonal.

Our findings offer an objective evaluation of transmission dynamics and epidemiological shifts in respiratory pathogens during the coronavirus disease 2019 pandemic in Kunming, serving as a basis for informed decision-making, prevention, and treatment strategies.

There are significant differences in the transmission rate and mortality rate of COVID-19 under environmental conditions such as seasons and climates. However, the impact of environmental factors on the role of the COVID-19 pandemic and the transmission mechanism of the SARS-CoV-2 is unclear. Therefore, a comprehensive understanding of the impact of environmental factors on COVID-19 can provide innovative insights for global epidemic prevention and control policies and COVID-19 related research. This review summarizes the evidence of the impact of different natural and social environmental factors on the transmission of COVID-19 through a comprehensive analysis of epidemiology and mechanism research. This will provide innovative inspiration for global epidemic prevention and control policies and provide reference for similar infectious diseases that may emerge in the future.

Evidence reveals mechanisms by which natural environmental factors influence the transmission of COVID-19, including (i) virus survival and transport, (ii) immune system damage, (iii) inflammation, oxidative stress, and cell death, and (iiii) increasing risk of complications. All of these measures appear to be effective in controlling the spread or mortality of COVID-19: (1) reducing air pollution levels, (2) rational use of ozone disinfection and medical ozone therapy, (3) rational exposure to sunlight, (4) scientific ventilation and maintenance of indoor temperature and humidity, (5) control of population density, and (6) control of population movement. Our review indicates that with the continuous mutation of SARS-CoV-2, high temperature, high humidity, low air pollution levels, and low population density more likely to slow down the spread of the virus.

Pathogens of the enterovirus genus, including poliovirus and coxsackieviruses, typically circulate in the summer months suggesting a possible positive association between warmer weather and transmission. Here we evaluate the environmental and demographic drivers of enterovirus transmission, as well as the implications of climate change for future enterovirus circulation. We leverage pre-vaccination era data on polio in the US as well as data on two enterovirus A serotypes in China and Japan that are known to cause hand, foot, and mouth disease. Using mechanistic modeling and statistical approaches, we find that enterovirus transmission appears positively correlated with temperature although demographic factors, particularly the timing of school semesters, remain important. We use temperature projections from Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate future outbreaks under late 21st-century climate change for Chinese provinces. We find that outbreak size increases with climate change on average, though results differ across climate models depending on the degree of wintertime warming. In the worst-case scenario, we project peak outbreaks in some locations could increase by up to 40%.

Like other tropical and subtropical regions, influenza viruses can circulate year-round in Hong Kong. However, during the COVID-19 pandemic, there was a significant decrease in influenza activity. The objective of this study was to retrospectively forecast influenza activity during the year 2020 and assess the impact of COVID-19 public health social measures (PHSMs) on influenza activity and hospital admissions in Hong Kong. Using weekly surveillance data on influenza virus activity in Hong Kong from 2010 to 2019, we developed a statistical modeling framework to forecast influenza virus activity and associated hospital admissions. We conducted short-term forecasts (1-4 weeks ahead) and medium-term forecasts (1-13 weeks ahead) for the year 2020, assuming no PHSMs were implemented against COVID-19. We estimated the reduction in transmissibility, peak magnitude, attack rates, and influenza-associated hospitalization rate resulting from these PHSMs. For short-term forecasts, mean ambient ozone concentration and school holidays were found to contribute to better prediction performance, while absolute humidity and ozone concentration improved the accuracy of medium-term forecasts. We observed a maximum reduction of 44.6% (95% CI: 38.6% - 51.9%) in transmissibility, 75.5% (95% CI: 73.0% - 77.6%) in attack rate, 41.5% (95% CI: 13.9% - 55.7%) in peak magnitude, and 63.1% (95% CI: 59.3% - 66.3%) in cumulative influenza-associated hospitalizations during the winter-spring period of the 2019/2020 season in Hong Kong. The implementation of PHSMs to control COVID-19 had a substantial impact on influenza transmission and associated burden in Hong Kong. Incorporating information on factors influencing influenza transmission improved the accuracy of our predictions.

To control resurging infectious diseases like mumps, it is necessary to resort to effective control and preventive measures. These measures include increasing vaccine coverage, providing the community with advice on how to reduce exposure, and closing schools. To justify such intervention, it is important to understand how well each of these measures helps to limit transmission.

In this paper, we propose a simple SEILR (susceptible-exposed-symptomatically infectious-asymptomatically infectious-recovered) model by using a novel transmission rate function to incorporate temperature, humidity, and closing school factors. This new transmission rate function allows us to verify the impact of each factor either separately or combined. Using reported mumps cases from 2004 to 2018 in the mainland of China, we perform data fitting and parameter estimation to evaluate the basic reproduction number  R 0 . As a wide range of one-dose measles, mumps, and rubella (MMR) vaccine programs in China started only in 2008, we use different vaccination proportions for the first Stage I period (from 2004 to 2008) and the second Stage II period (from 2009 to 2018). This allows us to verify the importance of higher vaccine coverage with a possible second dose of MMR vaccine.

We find that the basic reproduction number  R 0  is generally between 1 and 3. We then use the Akaike Information Criteria to assess the extent to which each of the three factors contributed to the spread of mumps. The findings suggest that the impact of all three factors is substantial, with temperature having the most significant impact, followed by school opening and closing, and finally humidity.

We conclude that the strategy of increasing vaccine coverage, changing micro-climate (temperature and humidity), and closing schools can greatly reduce mumps transmission.

Forecasting of seasonal mortality patterns can provide useful information for planning health-care demand and capacity. Timely mortality forecasts are needed during severe winter spikes and/or pandemic waves to guide policy-making and public health decisions. In this article, we propose a flexible method for forecasting all-cause mortality in real time considering short-term changes in seasonal patterns within an epidemiologic year. All-cause mortality data have the advantage of being available with less delay than cause-specific mortality data. In this study, we use all-cause monthly death counts obtained from the national statistical offices of Denmark, France, Spain, and Sweden from epidemic seasons 2012-2013 through 2021-2022 to demonstrate the performance of the proposed approach. The method forecasts deaths 1 month ahead, based on their expected ratio to the next month. Prediction intervals are obtained via bootstrapping. The forecasts accurately predict the winter mortality peaks before the COVID-19 pandemic. Although the method predicts mortality less accurately during the first wave of the COVID-19 pandemic, it captures the aspects of later waves better than other traditional methods. The method is attractive for health researchers and governmental offices for aiding public health responses because it uses minimal input data, makes simple and intuitive assumptions, and provides accurate forecasts both during seasonal influenza epidemics and during novel virus pandemics.

Airborne diseases due to climate change pose significant public health challenges in Bangladesh. Little was known about the spatio-temporal pattern of airborne diseases at the district level in the country. Therefore, this study aimed to investigate the spatio-temporal pattern and associated meteorological factors of airborne diseases in Bangladesh using exploratory analysis and spatial regression models.

This study used district-level reported cases of airborne diseases (meningococcal, measles, mumps, influenza, tuberculosis, and encephalitis) and meteorological data (temperature, relative humidity, wind speed, and precipitation) from 2017 to 2020. Geospatial mapping and spatial error regression models were utilized to analyze the data.

From 2017 to 2020, a total of 315 meningococcal, 5159 measles, 1341 mumps, 346 influenza, 4664 tuberculosis, and 229 encephalitis cases were reported in Bangladesh. Among airborne diseases, measles demonstrated the highest prevalence, featuring a higher incidence rate in the coastal Bangladeshi districts of Lakshmipur, Patuakhali, and Cox's Bazar, as well as in Maulvibazar and Bandarban districts from 2017 to 2020. In contrast, tuberculosis (TB) emerged as the second most prevalent disease, with a higher incidence rate observed in districts such as Khagrachhari, Rajshahi, Tangail, Bogra, and Sherpur. The spatial error regression model revealed that among climate variables, mean (β = 9.56, standard error [SE]: 3.48) and maximum temperature (β = 1.19, SE: 0.40) were significant risk factors for airborne diseases in Bangladesh. Maximum temperature positively influenced measles (β = 2.74, SE: 1.39), whereas mean temperature positively influenced both meningococcal (β = 5.57, SE: 2.50) and mumps (β = 11.99, SE: 3.13) diseases.

The findings from the study provide insights for planning early warning, prevention, and control strategies to combat airborne diseases in Bangladesh and similar endemic countries. Preventive measures and enhanced monitoring should be taken in some high-risk districts for airborne diseases in the country.

Forecasts of infectious agents provide public health officials advanced warning about the intensity and timing of the spread of disease. Past work has found that accuracy and calibration of forecasts is weakest when attempting to predict an epidemic peak. Forecasts from a mechanistic model would be improved if there existed accurate information about the timing and intensity of an epidemic. We presented 3000 humans with simulated surveillance data about the number of incident hospitalizations from a current and two past seasons, and asked that they predict the peak time and intensity of the underlying epidemic. We found that in comparison to two control models, a model including human judgment produced more accurate forecasts of peak time and intensity of hospitalizations during an epidemic. Chimeric models have the potential to improve our ability to predict targets of public health interest which may in turn reduce infectious disease burden.

Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection dynamics, presumably via heterosubtypic cross-immunity.

While many investigations examined the association between environmental covariates and COVID-19 incidence, none have examined their relationship with superspreading, a characteristic describing very few individuals disproportionally infecting a large number of people.

Contact tracing data of all the laboratory-confirmed COVID-19 cases in Hong Kong from February 16, 2020 to April 30, 2021 were used to form the infection clusters for estimating the time-varying dispersion parameter (kt), a measure of superspreading potential. Generalized additive models with identity link function were used to examine the association between negative-log kt (larger means higher superspreading potential) and the environmental covariates, adjusted with mobility metrics that account for the effect of social distancing measures.

A total of 6,645 clusters covering 11,717 cases were reported over the study period. After centering at the median temperature, a lower ambient temperature at 10th percentile (18.2 °C) was significantly associated with a lower estimate of negative-log kt (adjusted expected change: -0.239 [95 % CI: -0.431 to -0.048]). While a U-shaped relationship between relative humidity and negative-log kt was observed, an inverted U-shaped relationship with actual vapour pressure was found. A higher total rainfall was significantly associated with lower estimates of negative-log kt.

This study demonstrated a link between meteorological factors and the superspreading potential of COVID-19. We speculated that cold weather and rainy days reduced the social activities of individuals minimizing the interaction with others and the risk of spreading the diseases in high-risk facilities or large clusters, while the extremities of relative humidity may favor the stability and survival of the SARS-CoV-2 virus.

Biological material is routinely collected at crime scenes and from exhibits and is a key type of evidence during criminal investigations. Improvements in DNA technologies allow collection and profiling of trace samples, comprised of few cells, significantly expanding the types of exhibits targeted for DNA analysis to include touched surfaces. However, success rates from trace and touch DNA samples tend to be poorer compared to other biological materials such as blood. Simultaneously, there have been recent advances in the utility of environmental DNA collection (eDNA) in identification and tracking of different biological organisms and species from bacteria to naked mole rats in different environments, including, soil, ice, snow, air and aquatic. This paper examines the emerging methods and research into eDNA collection, with a special emphasis on the potential forensic applications of human DNA collection from air including challenges and further studies required to progress implementation.

Antibiotic use, particularly inappropriate use by irrational prescribing or over-the-counter purchases, is of great concern for China as it facilitates the spread of antibiotic resistances. In this study, we applied wastewater-based epidemiology (WBE) to monitor the total consumption of eight common antibiotics in three cities in northern, eastern and southern China. Wastewater samples were successively collected from 17 wastewater treatment plants including weekdays and weekends spanning four seasons between 2019 and 2021. The concentration of antibiotics and their corresponding metabolites showed a significant correlation, confirming the measured antibiotics were actually consumed. Different seasonal trends in antibiotic use were found among the cities. It was more prevalent in the winter in the northern city Beijing, with the high antibiotic consumption attributed to peak influenza occurrence in the city. This is clear evidence of irrational prescription of antibiotics since it's known that antibiotics do little to treat influenza. In terms of overall consumption, Foshan is significantly lower, thanks to warmer climate and higher use of herbal tea as a prevention measure. WBE estimates of antibiotic consumption were relatively comparable with other data sources, with azithromycin as the top antibiotic measured here. The studied cities had higher WBE estimated antibiotics consumption than results of previous studies in the literature. Monitoring antibiotic use in different areas and periods through WBE in combination with complementary information, can better inform appropriate antibiotic guideline policies in various regions and nations.

Human contact patterns are a key determinant driving the spread of respiratory infectious diseases. However, the relationship between contact patterns and seasonality as well as their possible association with the seasonality of respiratory diseases is yet to be clarified.

We investigated the association between temperature and human contact patterns using data collected through a cross-sectional diary-based contact survey in Shanghai, China, between December 24, 2017, and May 30, 2018. We then developed a compartmental model of influenza transmission informed by the derived seasonal trends in the number of contacts and validated it against A(H1N1)pdm09 influenza data collected in Shanghai during the same period.

We identified a significant inverse relationship between the number of contacts and the seasonal temperature trend defined as a spline interpolation of temperature data (p = 0.003). We estimated an average of 16.4 (95% PrI: 15.1-17.5) contacts per day in December 2017 that increased to an average of 17.6 contacts (95% PrI: 16.5-19.3) in January 2018 and then declined to an average of 10.3 (95% PrI: 9.4-10.8) in May 2018. Estimates of influenza incidence obtained by the compartmental model comply with the observed epidemiological data. The reproduction number was estimated to increase from 1.24 (95% CI: 1.21-1.27) in December to a peak of 1.34 (95% CI: 1.31-1.37) in January. The estimated median infection attack rate at the end of the season was 27.4% (95% CI: 23.7-30.5%).

Our findings support a relationship between temperature and contact patterns, which can contribute to deepen the understanding of the relationship between social interactions and the epidemiology of respiratory infectious diseases.