Weather and notified Campylobacter infections in temperate and sub-tropical regions of Australia: an ecological study

A systematic review and meta-analysis of ambient temperature and precipitation with infections from five food-borne bacterial pathogens

Studies on climate variables and food pathogens are either pathogen- or region-specific, necessitating a consolidated view on the subject. This study aims to systematically review all studies on the association of ambient temperature and precipitation on the incidence of gastroenteritis and bacteraemia from Salmonella, Shigella, Campylobacter, Vibrio, and Listeria species. PubMed, Ovid MEDLINE, Scopus, and Web of Science databases were searched up to 9 March 2023. We screened 3,204 articles for eligibility and included 83 studies in the review and three in the meta-analysis. Except for one study on Campylobacter, all showed a positive association between temperature and Salmonella, Shigella, Vibrio sp., and Campylobacter gastroenteritis. Similarly, most of the included studies showed that precipitation was positively associated with these conditions. These positive associations were found regardless of the effect measure chosen. The pooled incidence rate ratio (IRR) for the three studies that included bacteraemia from Campylobacter and Salmonella sp. was 1.05 (95 per cent confidence interval (95% CI): 1.03, 1.06) for extreme temperature and 1.09 (95% CI: 0.99, 1.19) for extreme precipitation. If current climate trends continue, our findings suggest these pathogens would increase patient morbidity, the need for hospitalization, and prolonged antibiotic courses.

Exploring the association of weather variability on Campylobacter - A systematic review

BACKGROUND

Previous work has found climate change-induced weather variability is suspected to increase the transmission of enteric pathogens, including Campylobacter, a leading cause of bacterial gastroenteritis. While the relationship between extreme weather events and diarrheal diseases has been documented, the specific impact on Campylobacter infections remains underexplored.

OBJECTIVE

To synthesize the peer-reviewed literature exploring the effect of weather variability on Campylobacter infections in humans.

METHODS

The review included English language, peer-reviewed articles, published up to September 1, 2022 in PubMed, Embase, GEOBASE, Agriculture and Environmental Science Database, and CABI Global Health exploring the effect of an antecedent weather event on human enteric illness caused by Campylobacter (PROSPERO Protocol # 351884). We extracted study information including data sources, methods, summary measures, and effect sizes. Quality and weight of evidence reported was summarized and bias assessed for each article.

RESULTS

After screening 278 articles, 47 articles (34 studies, 13 outbreak reports) were included in the evidence synthesis. Antecedent weather events included precipitation (n = 35), temperature (n = 30), relative humidity (n = 7), sunshine (n = 6), and El Niño and La Niña (n = 3). Reviewed studies demonstrated that increases in precipitation and temperature were correlated with Campylobacter infections under specific conditions, whereas low relative humidity and sunshine were negatively correlated. Articles estimating the effect of animal operations (n = 15) found presence and density of animal operations were significantly associated with infections. However, most of the included articles did not assess confounding by seasonality, presence of animal operations, or describe estimates of risk.

DISCUSSION

This review explores what is known about the influence of weather events on Campylobacter and identifies previously underreported negative associations between low relative humidity and sunshine on Campylobacter infections. Future research should explore pathogen-specific estimates of risk, which can be used to influence public health strategies, improve source attribution and causal pathways, and project disease burden due to climate change.

Associations between ambient temperature and enteric infections by pathogen: a systematic review and meta-analysis

BACKGROUND

Numerous studies have quantified the associations between ambient temperature and enteric infections, particularly all-cause enteric infections. However, the temperature sensitivity of enteric infections might be pathogen dependent. Here, we sought to identify pathogen-specific associations between ambient temperature and enteric infections.

METHODS

We did a systematic review and meta-analysis by searching PubMed, Web of Science, and Scopus for peer-reviewed research articles published from Jan 1, 2000, to Dec 31, 2019, and also hand searched reference lists of included articles and excluded reviews. We included studies that quantified the effects of ambient temperature increases on common pathogen-specific enteric infections in humans. We excluded studies that expressed ambient temperature as a categorical or diurnal range, or in a standardised format. Two authors screened the search results, one author extracted data from eligible studies, and four authors verified the data. We obtained the overall risks by pooling the relative risks of enteric infection by pathogen for each 1°C temperature rise using random-effects modelling and robust variance estimation for the correlated effect estimates. Between-study heterogeneity was measured using I², τ², and Q-statistic. Publication bias was determined using funnel plot asymmetry and the trim-and-fill method. Differences among pathogen-specific pooled estimates were determined using subgroup analysis of taxa-specific meta-analysis. The study protocol was not registered but followed the PRISMA guidelines.

FINDINGS

We identified 2981 articles via database searches and 57 articles from scanning reference lists of excluded reviews and included articles, of which 40 were eligible for pathogen-specific meta-analyses. The overall increased risks of incidence per 1°C temperature rise, expressed as relative risks, were 1·05 (95% CI 1·04-1·07; I² 97%) for salmonellosis, 1·07 (1·04-1·10; I² 99%) for shigellosis, 1·02 (1·01-1·04; I² 98%) for campylobacteriosis, 1·05 (1·04-1·07; I² 36%) for cholera, 1·04 (1·01-1·07; I² 98%) for Escherichia coli enteritis, and 1·15 (1·07-1·24; I² 0%) for typhoid. Reduced risks per 1°C temperature increase were 0·96 (95% CI 0·90-1·02; I² 97%) for rotaviral enteritis and 0·89 (0·81-0·99; I² 96%) for noroviral enteritis. There was evidence of between-pathogen differences in risk for bacterial infections but not for viral infections.

INTERPRETATION

Temperature sensitivity of enteric infections can vary according to the enteropathogen causing the infection, particularly for bacteria. Thus, we encourage a pathogen-specific health adaptation approach, such as vaccination, given the possibility of increasingly warm temperatures in the future.

FUNDING

Japan Society for the Promotion of Science (Kakenhi) Grant-in-Aid for Scientific Research.

Occurrence and multidrug resistance of Campylobacter spp. at duck farms and associated environmental and anthropogenic risk factors in Bangladesh

Background

The alarming rise in multi-drug resistant (MDR) zoonotic pathogens, including Campylobacter spp., has been threatening the health sector globally. In Bangladesh, despite rapid growth in poultry sector little is known about the potential risks of zoonotic pathogens in homestead duck flocks. The aim of this study was to understand the occurrence, species diversity, and multi-drug resistance in Campylobacter spp., and identify the associated risk factors in duck farms in Bangladesh.

Methods

The study involved 20 duck farms at 6 sub-districts of Mymensingh, Bangladesh. Monthly occurrence of Campylobacter spp. in potential sources at the farms during February-September, 2018, was detected by culture and PCR-based methods. Campylobacter isolates were examined for resistance to different antimicrobials. Risk factors, concerning climatic and environmental disposition, farm management, and anthropogenic practices, of Campylobacter infection were estimated by participatory epidemiological tools.

Results

Occurrence of Campylobacter spp. was detected in overall 36.90% (155/420) samples, more frequently in drinking water (60%, 30/50), followed by cloacal swab (37.50%, 75/200), egg surface swab (35%, 35/100) and soil of the duck resting places (30%, 15/50) but was not detected in feed samples (n = 20). PCR assays distinguished the majority (61.30%, 95/155) of the isolates as C. coli, while the rest (38.70%, 60/155) were C. jejuni. Notably, 41.7% (25/60) and 31.6% (30/95) strains of C. jejuni and C. coli, respectively, were observed to be MDR. The dynamics of Campylobacter spp., distinctly showing higher abundance during summer and late-monsoon, correlated significantly with temperature, humidity, and rainfall, while sunshine hours had a negative influence. Anthropogenic management-related factors, including, inadequate hygiene practices, use of untreated river water, wet duck shed, flock age (1–6 months), and unscrupulous use of antimicrobials were identified to enhance the risk of MDR Campylobacter infection.

Conclusion

The present study clearly demonstrates that duck farms contribute to the enhanced occurrence and spread of potentially pathogenic and MDR C. coli and C. jejuni strains and the bacterial dynamics are governed by a combined interaction of environmental and anthropogenic factors. A long-term holistic research at the environment-animal-human interface would be integral to divulge health risk reduction approaches tackling the spread of Campylobacter spp. from duck farms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06834-w.

The effect of meteorological variables on salmonellosis incidence in Kermanshah, West of Iran: a generalized linear model with negative binomial approach

PURPOSE

Salmonella is one of the main causes of gastroenteritis, and its incidence may be affected by meteorological variables. This is the first study about the effect of climatic factors on salmonella incidence in Kermanshah, Iran.

METHODS

Data about salmonellosis cases in Kermanshah were inquired from Center for Communicable Disease Control, at the Ministry of Health and Medical Education of Iran, for the 2008 to 2018 time-frame. Meteorological variables including maximum, minimum and mean of temperature and humidity, sunshine hours and rainfall were inquired for the same time frame. Negative binomial generalized linear models (GLM) were used to assess the effect of meteorological variables on the weekly incidence of salmonellosis.

RESULTS

During the years under study, 569 confirmed cases were registered in Kermanshah province. Study results showed a 3 % increase in salmonellosis incidence, after 1 % increase in minimum humidity in the week before (incidence rate ratio (IRR): 1.03; 95 % confidence interval (CI):1.02-1.05) and also a 4 % increase in incidence for 1 °C increase in mean temperature in the same week (IRR: 1.04; 95 % CI:1.02-1.06).

CONCLUSIONS

Increase in minimum humidity and mean temperature may have a role in increasing the incidence of salmonellosis in Iran.

Exploring Campylobacter seasonality across Europe using The European Surveillance System (TESSy), 2008 to 2016

BackgroundCampylobacteriosis is the most commonly reported food-borne infection in the European Union, with an annual number of cases estimated at around 9 million. In many countries, campylobacteriosis has a striking seasonal peak during early/mid-summer. In the early 2000s, several publications reported on campylobacteriosis seasonality across Europe and associations with temperature and precipitation. Subsequently, many European countries have introduced new measures against this food-borne disease.AimTo examine how the seasonality of campylobacteriosis varied across Europe from 2008-16, to explore associations with temperature and precipitation, and to compare these results with previous studies. We also sought to assess the utility of the European Surveillance System TESSy for cross-European seasonal analysis of campylobacteriosis.MethodsWard's Minimum Variance Clustering was used to group countries with similar seasonal patterns of campylobacteriosis. A two-stage multivariate meta-analysis methodology was used to explore associations with temperature and precipitation.ResultsNordic countries had a pronounced seasonal campylobacteriosis peak in mid- to late summer (weeks 29-32), while most other European countries had a less pronounced peak earlier in the year. The United Kingdom, Ireland, Hungary and Slovakia had a slightly earlier peak (week 24). Campylobacteriosis cases were positively associated with temperature and, to a lesser degree, precipitation.ConclusionAcross Europe, the strength and timing of campylobacteriosis peaks have remained similar to those observed previously. In addition, TESSy is a useful resource for cross-European seasonal analysis of infectious diseases such as campylobacteriosis, but its utility depends upon each country's reporting infrastructure.

Seasonality and the effects of weather on Campylobacter infections

Background

Campylobacteriosis is a major public health concern. The weather factors that influence spatial and seasonal distributions are not fully understood.

Methods

To investigate the impacts of temperature and rainfall on Campylobacter infections in England and Wales, cases of Campylobacter were linked to local temperature and rainfall at laboratory postcodes in the 30 days before the specimen date. Methods for investigation included a comparative conditional incidence, wavelet, clustering, and time series analyses.

Results

The increase of Campylobacter infections in the late spring was significantly linked to temperature two weeks before, with an increase in conditional incidence of 0.175 cases per 100,000 per week for weeks 17 to 24; the relationship to temperature was not linear. Generalized structural time series model revealed that changes in temperature accounted for 33.3% of the expected cases of Campylobacteriosis, with an indication of the direction and relevant temperature range. Wavelet analysis showed a strong annual cycle with additional harmonics at four and six months. Cluster analysis showed three clusters of seasonality with geographic similarities representing metropolitan, rural, and other areas.

Conclusions

The association of Campylobacteriosis with temperature is likely to be indirect. High-resolution spatial temporal linkage of weather parameters and cases is important in improving weather associations with infectious diseases. The primary driver of Campylobacter incidence remains to be determined; other avenues, such as insect contamination of chicken flocks through poor biosecurity should be explored.

Electronic supplementary material

The online version of this article (10.1186/s12879-019-3840-7) contains supplementary material, which is available to authorized users.

Interrelationships between Multiple Climatic Factors and Incidence of Foodborne Diseases

Climatic factors can affect the incidence of foodborne diseases (FBDs). Moreover, microbial network inference is useful for predicting the interrelationships between the incidence of FBDs and climatic factors. However, the interrelationships between FBD pathogens and most climatic factors are unknown. Using principal component analysis (PCA) and partial correlation coefficient matrices (PCCMs), we determined the intra-ecosystem interrelationship network of the multiple combined effects of 5 climatic factors (temperature, relative humidity, rainfall, insolation, and cloudiness) and the monthly incidences of 12 bacterial FBDs. Many FBD pathogens are interrelated with multiple combined factors. Salmonellosis has strong positive interrelationships with Vibrio parahaemolyticus and enterohemorrhagic Escherichia coli, and the interrelationships between Staphylococcus aureus/enteropathogenic E. coli/enterotoxigenic E. coli exhibits a typical triangular pattern with the combined effects of all 5 climatic factors. Meanwhile, campylobacteriosis and Clostridium perfringens infections are negatively interrelated with insolation and cloudiness. Enteroinvasive E. coli, Bacillus cereus, Listeria spp., and Yersinia enterocolitica are significantly interrelated with any climatic factor combination. The interrelationships or higher-order interrelationships among these climatic factors play an important role in the incidence of FBDs, although the underlying mechanisms remain unclear. Our results will serve as a foundation for more sophisticated models of future FBD patterns with regard to climate change.

A six-year epidemiological surveillance study in Split-Dalmatia County, Croatia: urban versus rural differences in human campylobacteriosis incidence

Data from population-based laboratory surveillance were used to examine the epidemiological pattern of campylobacteriosis in a sentinel site, Split-Dalmatia County (SDC),Croatia, from 2007 to 2012, and to evaluate the association between disease incidence and demographic, geographical, climatic, agricultural, and microbiological factors. A total of 2658 laboratory-confirmed Campylobacter infections were recorded. Overall mean incidence was 96/100,000, ranging from 61/100,000 in rural to 131/100,000 in urban areas; rates were highest in the age group 0-4 years. Overall mean and age- and sex-specific incidences were significantly higher in urban versus rural areas (p < 0.01). The number of infections peaked in early summer, and was correlated with higher average monthly temperature (r = 0.58) and lower humidity (r = - 0.27), but not with precipitation. Incidence was not associated with agricultural activities. A distinct campylobacteriosis pattern with consistently higher urban versus rural incidence was observed, which may help formulate further preventive measures.

Ambient temperature and age-related notified Campylobacter infection in Israel: A 12-year time series study

BACKGROUND

Campylobacter spp. are the leading cause of foodborne infection worldwide, with a seasonal disease peak that might be affected by temperature increase. We studied the relationship between ambient temperature and weekly notified Campylobacter spp.infections.

METHODS

Data on 29,762 laboratory-confirmed cases of Campylobacter infection for the period, January, 1999 to December, 2010 were retrieved from the Ministry of Health registry. To estimate the association between the number of weekly cases of Campylobacter infection and the national average temperature at lags 0-3 weeks, firstly, we used GAM models, and secondly two-segment piecewise linear Poisson regressions. The effect of temperature was adjusted for seasonality, long-term trends and holidays.

RESULTS

We found a J-shaped relationship between ambient temperature and notified Campylobacter spp.

CASES

For C. jejuni in all ages, the curve below the threshold was constant and the percent increase in cases for 1 °C above a threshold of 27 °C was 15.4% (95%CI: 6.7-24.1%). For ages 3-10 yr and > =26 yr the curve was constant below the threshold and positive above it; the percent increase in cases for 1 °C was 17.7%(95%CI: 6.0-29.4%) and 23.7%(95%CI: 11.6-35.8%), respectively. For ages 0-2 yr the curve was linear with no threshold and the percent increase for 1 °C was 5.1%(95%CI: 2.1-8.1%). For ages 11-25 yr the curve was always constant. Results for C. coli were similar.

CONCLUSION

Our findings indicate that higher temperatures throughout the year affect Campylobacter spp. morbidity, especially in younger children. This should be taken into consideration in public education and health system preparedness for temperature increases as a result of climate change.

Weather correlates of Campylobacter prevalence in broilers at slaughter under tropical conditions in Sri Lanka

Campylobacter is the primary agent of human bacterial gastroenteritis worldwide. In contrast to temperate zones, weather effects on Campylobacter prevalence in broilers under tropical conditions are under-researched. We examined the association between weather and Campylobacter prevalence in slaughtered broilers in Sri Lanka, a tropical country with weather variations led by monsoons. Each month (October 2009-July 2011), 20-30 broiler batches referring to two semi-automated slaughterhouses from five Sri Lankan provinces were tested for Campylobacter contamination and analysed in relation to temperature, humidity and rainfall. Overall prevalence was 63.8% (95% CI 59.6-67.9%, n = 542), peaking in September-November. Each 1 °C increase in monthly mean temperature up to 26 °C increased Campylobacter-positive batches by 16.4% (95% CI 0.4-35.1%). For each 10 mm increase in monthly total rainfall up to 300 mm, Campylobacter-positive batches increased significantly by 0.8% (0.1-1.5%) at 1-month lag. For each 1% increase in relative humidity up to 80% at 1- and 2-month lags, Campylobacter-positive batches increased of respectively 4.2% (1.9-6.7%) and 4.0% (1.5-6.5), and decreased by 3.6% (2.6-4.6%) and 4.0% (2.6-5.4%) for unit increases above 80%. These results suggest that even in tropical countries without marked seasons, there are weather effects possibly reflecting Campylobacter potential to colonise its preferred host and/or survive in the environment.

The effects of ambient temperature and heatwaves on dailyCampylobactercases in Adelaide, Australia, 1990–2012

Campylobacterspp. is a commonly reported food-borne disease with major consequences for morbidity. In conjunction with predicted increases in temperature, proliferation in the survival of microorganisms in hotter environments is expected. This is likely to lead, in turn, to an increase in contamination of food and water and a rise in numbers of cases of infectious gastroenteritis. This study assessed the relationship ofCampylobacterspp. with temperature and heatwaves, in Adelaide, South Australia.

We estimated the effect of (i) maximum temperature and (ii) heatwaves on dailyCampylobactercases during the warm seasons (1 October to 31 March) from 1990 to 2012 using Poisson regression models.

There was no evidence of a substantive effect of maximum temperature per 1 °C rise (incidence rate ratio (IRR) 0·995, 95% confidence interval (95% CI) 0·993–0·997) nor heatwaves (IRR 0·906, 95% CI 0·800–1·026) onCampylobactercases. In relation to heatwave intensity, which is the daily maximum temperature during a heatwave, notifications decreased by 19% within a temperature range of 39–40·9 °C (IRR 0·811, 95% CI 0·692–0·952). We found little evidence of an increase in risk and lack of association betweenCampylobactercases and temperature or heatwaves in the warm seasons. Heatwave intensity may play a role in that notifications decreased with higher temperatures. Further examination of the role of behavioural and environmental factors in an effort to reduce the risk of increasedCampylobactercases is warranted.

Geographical Environment Factors and Risk Assessment of Tick-Borne Encephalitis in Hulunbuir, Northeastern China

Tick-borne encephalitis (TBE) is one of natural foci diseases transmitted by ticks. Its distribution and transmission are closely related to geographic and environmental factors. Identification of environmental determinates of TBE is of great importance to understanding the general distribution of existing and potential TBE natural foci. Hulunbuir, one of the most severe endemic areas of the disease, is selected as the study area. Statistical analysis, global and local spatial autocorrelation analysis, and regression methods were applied to detect the spatiotemporal characteristics, compare the impact degree of associated factors, and model the risk distribution using the heterogeneity. The statistical analysis of gridded geographic and environmental factors and TBE incidence show that the TBE patients mainly occurred during spring and summer and that there is a significant positive spatial autocorrelation between the distribution of TBE cases and environmental characteristics. The impact degree of these factors on TBE risks has the following descending order: temperature, relative humidity, vegetation coverage, precipitation and topography. A high-risk area with a triangle shape was determined in the central part of Hulunbuir; the low-risk area is located in the two belts next to the outside edge of the central triangle. The TBE risk distribution revealed that the impact of the geographic factors changed depending on the heterogeneity.

Time series models of environmental exposures: Good predictions or good understanding

Time series data are popular in environmental epidemiology as they make use of the natural experiment of how changes in exposure over time might impact on disease. Many published time series papers have used parameter-heavy models that fully explained the second order patterns in disease to give residuals that have no short-term autocorrelation or seasonality. This is often achieved by including predictors of past disease counts (autoregression) or seasonal splines with many degrees of freedom. These approaches give great residuals, but add little to our understanding of cause and effect. We argue that modelling approaches should rely more on good epidemiology and less on statistical tests. This includes thinking about causal pathways, making potential confounders explicit, fitting a limited number of models, and not over-fitting at the cost of under-estimating the true association between exposure and disease.

Climatic factors and prevalence of Campylobacter in commercial broiler flocks in Thailand

Campylobacter are bacteria associated with human foodborne disease worldwide. Poultry and poultry products are generally considered as a main source of these organisms. Compared to temperate zones, baseline information on Campylobacter in tropical regions is limited. Thus, the objectives of the present study were 1) to determine the prevalence of Campylobacter in Thai broiler flocks and 2) to investigate the association between climatic factors (i.e., rainfall, ambient temperature, and relative humidity) and Campylobacter colonization status of broiler flocks in Thailand. A total of 442 commercial broiler flocks reared in the central and northeastern regions of Thailand during 2012 to 2014 were investigated. Campylobacter positive status was identified in 252 examined flocks (57.01%; 95% CI 52.39 to 61.63%). Prevalence of Campylobacter in the northeastern region (54.46%; 95% CI 44.76 to 63.83%) was slightly lower than that of the central region (57.77%; 95% CI 52.47 to 62.90%). More than 65% of Campylobacter positive flocks in the central and northeastern regions had within-flock prevalence higher than 75%. Generalized estimating equations (GEE) revealed that the increased rainfall and relative humidity were associated with the increase of Campylobacter colonization in broiler flocks (P ≤ 0.05), while no relationship between ambient temperature and Campylobacter colonization status was identified.

Extreme precipitation events and increased risk of campylobacteriosis in Maryland, U.S.A

Consumption of contaminated poultry, raw milk and water are significant risk factors for Campylobacter infection. Previous studies also have investigated the association between weather (temperature and precipitation) and increased risk of campylobacteriosis, but limited information exists regarding the impacts of extreme heat and precipitation events on campylobacteriosis risk, and how such risk may differentially impact coastal communities. We obtained Campylobacter case data 2002-2012; n=4804) from the Maryland Foodborne Diseases Active Surveillance Network (FoodNet). We identified extreme heat and extreme precipitation events during this time (2002-2012) using location and calendar day specific thresholds (95th percentile for extreme heat and 90th percentile for extreme precipitation) that were computed based on a 30-year baseline (1960-1989). We linked these datasets using GIS and used negative binomial generalized estimating equations adjusted for demographic confounders to calculate the association between exposure to extreme events and risk of campylobacteriosis in Maryland. We observed that a one-day increase in exposure to extreme precipitation events was associated with a 3% increase in risk of campylobacteriosis in coastal areas of Maryland (Incidence Rate Ratio (IRR): 1.03, 95% confidence interval (CI): 1.01, 1.05), but such an association was not observed in noncoastal areas. Furthermore, the risk associated with extreme precipitation events was considerably higher during La Niña periods (IRR: 1.09, 95% CI: 1.05, 1.13), while there was no evidence of elevated risk during El Niño or ENSO Neutral periods. Exposure to extreme heat events was not associated with an increased risk of campylobacteriosis, except during La Niña periods (IRR: 1.04, 95% CI: 1.01, 1.08). Extreme precipitation events could result in flooding within coastal areas that may bring water contaminated with bacterial pathogens (originating from sources such as septic systems, municipal wastewater treatment plants and concentrated animal feeding operations) into close proximity with individuals, where frequency of contact may be higher.

Untangling the Impacts of Climate Change on Waterborne Diseases: a Systematic Review of Relationships between Diarrheal Diseases and Temperature, Rainfall, Flooding, and Drought

Global climate change is expected to affect waterborne enteric diseases, yet to date there has been no comprehensive, systematic review of the epidemiological literature examining the relationship between meteorological conditions and diarrheal diseases. We searched PubMed, Embase, Web of Science, and the Cochrane Collection for studies describing the relationship between diarrheal diseases and four meteorological conditions that are expected to increase with climate change: ambient temperature, heavy rainfall, drought, and flooding. We synthesized key areas of agreement and evaluated the biological plausibility of these findings, drawing from a diverse, multidisciplinary evidence base. We identified 141 articles that met our inclusion criteria. Key areas of agreement include a positive association between ambient temperature and diarrheal diseases, with the exception of viral diarrhea and an increase in diarrheal disease following heavy rainfall and flooding events. Insufficient evidence was available to evaluate the effects of drought on diarrhea. There is evidence to support the biological plausibility of these associations, but publication bias is an ongoing concern. Future research evaluating whether interventions, such as improved water and sanitation access, modify risk would further our understanding of the potential impacts of climate change on diarrheal diseases and aid in the prioritization of adaptation measures.

Effect of temperature and precipitation on salmonellosis cases in South-East Queensland, Australia: an observational study

OBJECTIVE

Foodborne illnesses in Australia, including salmonellosis, are estimated to cost over $A1.25 billion annually. The weather has been identified as being influential on salmonellosis incidence, as cases increase during summer, however time series modelling of salmonellosis is challenging because outbreaks cause strong autocorrelation. This study assesses whether switching models is an improved method of estimating weather-salmonellosis associations.

DESIGN

We analysed weather and salmonellosis in South-East Queensland between 2004 and 2013 using 2 common regression models and a switching model, each with 21-day lags for temperature and precipitation.

RESULTS

The switching model best fit the data, as judged by its substantial improvement in deviance information criterion over the regression models, less autocorrelated residuals and control of seasonality. The switching model estimated a 5 °C increase in mean temperature and 10 mm precipitation were associated with increases in salmonellosis cases of 45.4% (95% CrI 40.4%, 50.5%) and 24.1% (95% CrI 17.0%, 31.6%), respectively.

CONCLUSIONS

Switching models improve on traditional time series models in quantifying weather-salmonellosis associations. A better understanding of how temperature and precipitation influence salmonellosis may identify where interventions can be made to lower the health and economic costs of salmonellosis.

A systematic review and meta-analysis of ambient temperature and diarrhoeal diseases

BACKGROUND

Global climate change is expected to increase the risk of diarrhoeal diseases, a leading cause of childhood mortality. However, there is considerable uncertainty about the magnitude of these effects and which populations bear the greatest risks.

METHODS

We conducted a systematic review using defined search terms across four major databases and, additionally, examined the references of 54 review articles captured by the search. We evaluated sources of heterogeneity by pathogen taxon, exposure measure, study quality, country income level and regional climate, and estimated pooled effect estimates for the subgroups identified in the heterogeneity analysis, using meta-analysis methods.

RESULTS

We identified 26 studies with 49 estimates. Pathogen taxa were a source of heterogeneity. There was a positive association between ambient temperature and all-cause diarrhoea (incidence rate ratio (IRR) 1.07; 95% confidence interval (CI) 1.03, 1.10) and bacterial diarrhoea (IRR 1.07; 95% CI 1.04, 1.10), but not viral diarrhoea (IRR 0.96; 95% CI 0.82, 1.11). These associations were observed in low-, middle- and high-income countries. Only one study of protozoan diarrhoea was identified.

CONCLUSIONS

Changes in temperature due to global climate change can and may already be affecting diarrhoeal disease incidence. The vulnerability of populations may depend, in part, on local pathogen distribution. However, evidence of publication bias and the uneven geographical distribution of studies limit the precision and generalizability of the pooled estimates.

The effect of temperature on different Salmonella serotypes during warm seasons in a Mediterranean climate city, Adelaide, Australia

Changing trends in foodborne disease are influenced by many factors, including temperature. Globally and in Australia, warmer ambient temperatures are projected to rise if climate change continues. Salmonella spp. are a temperature-sensitive pathogen and rising temperature can have a substantial effect on disease burden affecting human health. We examined the relationship between temperature and Salmonella spp. and serotype notifications in Adelaide, Australia. Time-series Poisson regression models were fit to estimate the effect of temperature during warmer months on Salmonella spp. and serotype cases notified from 1990 to 2012. Long-term trends, seasonality, autocorrelation and lagged effects were included in the statistical models. Daily Salmonella spp. counts increased by 1·3% [incidence rate ratio (IRR) 1·013, 95% confidence interval (CI) 1·008-1·019] per 1 °C rise in temperature in the warm season with greater increases observed in specific serotype and phage-type cases ranging from 3·4% (IRR 1·034, 95% CI 1·008-1·061) to 4·4% (IRR 1·044, 95% CI 1·024-1·064). We observed increased cases of S. Typhimurium PT9 and S. Typhimurium PT108 notifications above a threshold of 39 °C. This study has identified the impact of warm season temperature on different Salmonella spp. strains and confirms higher temperature has a greater effect on phage-type notifications. The findings will contribute targeted information for public health policy interventions, including food safety programmes during warmer weather.

Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability

Introduction

Each year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF), a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explore the roles of imported cases, mosquito density and climate variability in dengue transmission in China. The study was to identify the relationship between dengue occurrence and possible risk factors and to develop a predicting model for dengue’s control and prevention purpose.

Methodology and Principal Findings

Three traditional suburbs and one district with an international airport in Guangzhou city were selected as the study areas. Autocorrelation and cross-correlation analysis were used to perform univariate analysis to identify possible risk factors, with relevant lagged effects, associated with local dengue cases. Principal component analysis (PCA) was applied to extract principal components and PCA score was used to represent the original variables to reduce multi-collinearity. Combining the univariate analysis and prior knowledge, time-series Poisson regression analysis was conducted to quantify the relationship between weather variables, Breteau Index, imported DF cases and the local dengue transmission in Guangzhou, China. The goodness-of-fit of the constructed model was determined by pseudo-R², Akaike information criterion (AIC) and residual test. There were a total of 707 notified local DF cases from March 2006 to December 2012, with a seasonal distribution from August to November. There were a total of 65 notified imported DF cases from 20 countries, with forty-six cases (70.8%) imported from Southeast Asia. The model showed that local DF cases were positively associated with mosquito density, imported cases, temperature, precipitation, vapour pressure and minimum relative humidity, whilst being negatively associated with air pressure, with different time lags.

Conclusions

Imported DF cases and mosquito density play a critical role in local DF transmission, together with weather variables. The establishment of an early warning system, using existing surveillance datasets will help to control and prevent dengue in Guangzhou, China.

Climate variability, weather and enteric disease incidence in New Zealand: time series analysis

BACKGROUND

Evaluating the influence of climate variability on enteric disease incidence may improve our ability to predict how climate change may affect these diseases.

OBJECTIVES

To examine the associations between regional climate variability and enteric disease incidence in New Zealand.

METHODS

Associations between monthly climate and enteric diseases (campylobacteriosis, salmonellosis, cryptosporidiosis, giardiasis) were investigated using Seasonal Auto Regressive Integrated Moving Average (SARIMA) models.

RESULTS

No climatic factors were significantly associated with campylobacteriosis and giardiasis, with similar predictive power for univariate and multivariate models. Cryptosporidiosis was positively associated with average temperature of the previous month (β =  0.130, SE =  0.060, p <0.01) and inversely related to the Southern Oscillation Index (SOI) two months previously (β =  -0.008, SE =  0.004, p <0.05). By contrast, salmonellosis was positively associated with temperature (β  = 0.110, SE = 0.020, p<0.001) of the current month and SOI of the current (β  = 0.005, SE = 0.002, p<0.050) and previous month (β  = 0.005, SE = 0.002, p<0.05). Forecasting accuracy of the multivariate models for cryptosporidiosis and salmonellosis were significantly higher.

CONCLUSIONS

Although spatial heterogeneity in the observed patterns could not be assessed, these results suggest that temporally lagged relationships between climate variables and national communicable disease incidence data can contribute to disease prediction models and early warning systems.

The association of weather and bathing water quality on the incidence of gastrointestinal illness in the west of Scotland

The associations with weather and bathing water quality on infectious intestinal disease (IID) were investigated using data from two Scottish NHS Board areas. Monthly counts of viral and non-viral gastrointestinal infections were modelled as a smooth function of temperature, relative humidity and average monthly counts of faecal indicator organisms, respectively, adjusting for season and long-term trend effects. Strong seasonal patterns were observed for each group of pathogens. Peak viral gastrointestinal infection was in May while that of non-viral gastrointestinal infections was in July. A statistically significant negative association existed between weather (temperature and humidity) and viral infection. Average levels of non-viral gastrointestinal infections increased as temperature and relative humidity increased. Increasing levels of faecal indicator organisms in bathing waters were also associated with an increase in the average number of viral and non-viral gastrointestinal infections at the ecological level. Future climate change and prolonged precipitation events may result in increasing levels of faecal indicator organisms in bathing waters leading to likely increases in IIDs.

Environmental variability and the transmission of haemorrhagic fever with renal syndrome in Changsha, People's Republic of China

The transmission of haemorrhagic fever with renal syndrome (HFRS) is influenced by climatic, reservoir and environmental variables. The epidemiology of the disease was studied over a 6-year period in Changsha. Variables relating to climate, environment, rodent host distribution and disease occurrence were collected monthly and analysed using a time-series adjusted Poisson regression model. It was found that the density of the rodent host and multivariate El Niño Southern Oscillation index had the greatest effect on the transmission of HFRS with lags of 2–6 months. However, a number of climatic and environmental factors played important roles in affecting the density and transmission potential of the rodent host population. It was concluded that the measurement of a number of these variables could be used in disease surveillance to give useful advance warning of potential disease epidemics.

Seasonality in human zoonotic enteric diseases: a systematic review

Background

Although seasonality is a defining characteristic of many infectious diseases, few studies have described and compared seasonal patterns across diseases globally, impeding our understanding of putative mechanisms. Here, we review seasonal patterns across five enteric zoonotic diseases: campylobacteriosis, salmonellosis, vero-cytotoxigenic Escherichia coli (VTEC), cryptosporidiosis and giardiasis in the context of two primary drivers of seasonality: (i) environmental effects on pathogen occurrence and pathogen-host associations and (ii) population characteristics/behaviour.

Methodology/Principal Findings

We systematically reviewed published literature from 1960–2010, resulting in the review of 86 studies across the five diseases. The Gini coefficient compared temporal variations in incidence across diseases and the monthly seasonality index characterised timing of seasonal peaks. Consistent seasonal patterns across transnational boundaries, albeit with regional variations was observed. The bacterial diseases all had a distinct summer peak, with identical Gini values for campylobacteriosis and salmonellosis (0.22) and a higher index for VTEC (Gini = 0.36). Cryptosporidiosis displayed a bi-modal peak with spring and summer highs and the most marked temporal variation (Gini = 0.39). Giardiasis showed a relatively small summer increase and was the least variable (Gini = 0.18).

Conclusions/Significance

Seasonal variation in enteric zoonotic diseases is ubiquitous, with regional variations highlighting complex environment-pathogen-host interactions. Results suggest that proximal environmental influences and host population dynamics, together with distal, longer-term climatic variability could have important direct and indirect consequences for future enteric disease risk. Additional understanding of the concerted influence of these factors on disease patterns may improve assessment and prediction of enteric disease burden in temperate, developed countries.

Climate Change Impact Assessment of Food- and Waterborne Diseases

The PubMed and ScienceDirect bibliographic databases were searched for the period of 1998-2009 to evaluate the impact of climatic and environmental determinants on food- and waterborne diseases. The authors assessed 1,642 short and concise sentences (key facts), which were extracted from 722 relevant articles and stored in a climate change knowledge base. Key facts pertaining to temperature, precipitation, water, and food for 6 selected pathogens were scrutinized, evaluated, and compiled according to exposure pathways. These key facts (corresponding to approximately 50,000 words) were mapped to 275 terminology terms identified in the literature, which generated 6,341 connections. These relationships were plotted on semantic network maps to examine the interconnections between variables. The risk of campylobacteriosis is associated with mean weekly temperatures, although this link is shown more strongly in the literature relating to salmonellosis. Irregular and severe rain events are associated with Cryptosporidium sp. outbreaks, while noncholera Vibrio sp. displays increased growth rates in coastal waters during hot summers. In contrast, for Norovirus and Listeria sp. the association with climatic variables was relatively weak, but much stronger for food determinants. Electronic data mining to assess the impact of climate change on food- and waterborne diseases assured a methodical appraisal of the field. This climate change knowledge base can support national climate change vulnerability, impact, and adaptation assessments and facilitate the management of future threats from infectious diseases. In the light of diminishing resources for public health this approach can help balance different climate change adaptation options.

Campylobacter epidemiology: a descriptive study reviewing 1 million cases in England and Wales between 1989 and 2011

OBJECTIVES

To review Campylobacter cases in England and Wales over 2 decades and examine the main factors/mechanisms driving the changing epidemiology.

DESIGN

A descriptive study of Campylobacter patients between 1989 and 2011. Cases over 3 years were linked anonymously to postcode, population density, deprivation indices and census data. Cases over 5 years were anonymously linked to local weather exposure estimates.

SETTING

Patients were from general practice, hospital and environmental health investigations through primary diagnostic laboratories across England and Wales.

PARTICIPANTS

There were 1 109 406 cases.

OUTCOME MEASURES

Description of changes in Campylobacter epidemiology over 23 years and how the main drivers may influence these.

RESULTS

There was an increase in Campylobacter cases over the past 23 years, with the largest increase in people over 50 years. Changes in the underlying population have contributed to this, including the impacts of population increases after World War I, World War II and the 'baby boom' of the 1960s. A recent increase in risk or ascertainment within this population has caused an increase in cases in all age groups from 2004 to 2011. The seasonal increase in cases between weeks 18 (Early May) and 22 (Early June) was consistent across ages, years and regions and was most marked in children and in more rural regions. Campylobacter prevalence by week in each region correlated with temperature 2 weeks before. There were higher prevalences in areas with a low population density, low deprivation and lower percentage of people of ethnic origin. Data from sero-phage and multilocus sequence typing show a few common types and many uncommon types.

CONCLUSIONS

The drivers/mechanisms influencing seasonality, age distribution, population density, socioeconomic and long-term differences are diverse and their relative contributions remain to be established. Surveillance and typing provide insights into Campylobacter epidemiology and sources of infection, providing a sound basis for targeted interventions.

The prevalence of Campylobacter amongst a free-range broiler breeder flock was primarily affected by flock age

Campylobacter successfully colonizes broiler chickens, but little is known about the longer term natural history of colonization, since most flocks are slaughtered at an immature age. In this study, the prevalence and genetic diversity of Campylobacter colonizing a single free-range broiler breeder flock was investigated over the course of a year. The age of the flock was the most important factor in determining both the prevalence and diversity of Campylobacter over time. There was no correlation with season, temperature, the amount of rain and sunshine, or the dynamics of colonization amongst geographically and temporally matched broiler flocks. The higher prevalence rates coincided with the age at which broiler chickens are typically slaughtered, but then in the absence of bio-security or other intervention methods, and despite changes in flock management, the prevalence fell to significantly lower levels for the remainder of the study. The genetic diversity of Campylobacter increased as the flock aged, implying that genotypes were accumulated within the flock and may persist for a long time. A better understanding of the ecology of Campylobacter within commercial chicken flocks will allow the design of more effective farm-based interventions.

Long-term prediction of emergency department revenue and visitor volume using autoregressive integrated moving average model

This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive integrated moving average (ARIMA) model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume.

The spatial and temporal determinants of campylobacteriosis notifications in New Zealand, 2001–2007

Despite recent improvements, New Zealand still has one of the highest per-capita incidence rates of campylobacteriosis in the world. To reduce the incidence, a thorough understanding of the epidemiology of infection is needed. This retrospective analysis of 36 000 notified human cases during a high-risk period between 2001 and 2007 explored the spatial and temporal determinants of Campylobacter notifications at a fine spatial scale in order to improve understanding of the complex epidemiology. Social deprivation was associated with a decreased risk of notification, whereas urban residence was associated with an increased risk. However, for young children rural residence was a risk factor. High dairy cattle density was associated with an increased risk of notification in two of the three regions investigated. Campylobacter notification patterns exhibit large temporal variations; however, few factors were associated with periods of increased risk, in particular temperature did not appear to drive the seasonality in campylobacteriosis.

Effects of climatic elements on Campylobacter-contaminated chicken products in Japan

Japanese weather data for areas that produced Campylobacter spp.-positive chicken products were compared with those for areas producing negative samples. Regarding samples produced during the period of rising temperature (spring and summer), the mean weekly air temperatures for Campylobacter-positive samples were higher than those for negative samples for the period of the week in which the samples were purchased (18·7°C vs. 13·1°C, P=0·006) to a 12-week lag (12 weeks before purchasing samples; 7·9°C vs. 3·4°C, P=0·009). Significant differences in weekly mean minimum humidity and sunshine duration per day were also observed for 1- and 2-week lag periods. We postulated that the high air temperature, high humidity and short duration of sunshine for the chicken-rearing period increased Campylobacter colonization in chickens during the period of rising temperature. Consequently, the number of Campylobacter-contaminated chicken products on the market in Japan may fluctuate because of the climatic conditions to which reared chickens are exposed.

The reported incidence of campylobacteriosis modelled as a function of earlier temperatures and numbers of cases, Montreal, Canada, 1990-2006

Previous studies have detected an effect of earlier temperatures on the incidence of campylobacteriosis in humans, but without adjustment for earlier numbers of cases of the disease. We estimated the effect of temperature on the number of cases notified by week in Montreal, Canada, from 1 January 1990 to 26 March 2006, simultaneously with the effect of the numbers of cases notified in the preceding weeks. The current campylobacteriosis count (week 0) was modelled by negative binomial regression, with earlier weekly average temperatures and earlier counts as predictors. Secular trends were accounted for by cubic spline functions and seasonal variations by sine-cosine functions. Indicator variables identified weeks with fewer than 5 working days. In the final statistical model, a 1°C increase in temperature above 10°C during any of weeks -1 to -6 was associated with a 0.8% (95% CI: 0.3% to 1.3%) increase in the current count. For each additional notified case during any of weeks -1 to -5 or -9 to -12, the increase in the current count was approximately 0.5% (95% CI: 0.2% to 1.0%). Thus, earlier temperatures and earlier counts have independent effects, that of temperatures being the larger one. The temperature effect is too small to require short term public health planning. However, in Montreal, an increase in average temperature of the order of 4.5°C, forecast by some for 2055, could produce a 23% increase in incidence, resulting in about 4,000 excess cases per year.

Weather variability and paediatric infectious gastroenteritis

Investigations of the relationship between weather variability and infectious gastroenteritis (IG) are becoming increasingly important in light of international interest in the potential health effects of climate change. However, few studies have examined the impact on children, despite the fact that children are considered particularly vulnerable to climate change. We acquired data about cases of IG in children aged <15 years and about weather variability in Fukuoka, Japan from 2000 to 2008 and used time-series analyses to assess how weather variability affected IG cases, adjusting for confounding factors. The temperature-IG relationship had an inverted V shape, with fewer cases at temperatures lower and higher than ~13°C. Every 1°C increase in temperature below the threshold (13°C) was associated with a 23·2% [95% confidence interval (CI) 16·6-30·2] increase, while every 1°C increase in temperature above the threshold (13°C) was associated with an 11·8% (95% CI 6·6-17·3) decrease in incidence. The increase in cases per 1% drop in relative humidity was 3·9% (95% CI 2·8-5·0). The percentage increase of IG cases was greatest in the 0-4 years age group and tended to decrease with increasing age. We found a progressive reduction in weather-related IG cases in children aged >4 years. Our results suggest that public health interventions aimed at controlling weather-related IG may be most effective when focused on young children.

Climate variability and hemorrhagic fever with renal syndrome transmission in Northeastern China

BACKGROUND

The transmission of hemorrhagic fever with renal syndrome (HFRS) is influenced by climatic variables. However, few studies have examined the quantitative relationship between climate variation and HFRS transmission.

OBJECTIVE

We examined the potential impact of climate variability on HFRS transmission and developed climate-based forecasting models for HFRS in northeastern China.

METHODS

We obtained data on monthly counts of reported HFRS cases in Elunchun and Molidawahaner counties for 1997-2007 from the Inner Mongolia Center for Disease Control and Prevention and climate data from the Chinese Bureau of Meteorology. Cross-correlations assessed crude associations between climate variables, including rainfall, land surface temperature (LST), relative humidity (RH), and the multivariate El Niño Southern Oscillation (ENSO) index (MEI) and monthly HFRS cases over a range of lags. We used time-series Poisson regression models to examine the independent contribution of climatic variables to HFRS transmission.

RESULTS

Cross-correlation analyses showed that rainfall, LST, RH, and MEI were significantly associated with monthly HFRS cases with lags of 3-5 months in both study areas. The results of Poisson regression indicated that after controlling for the autocorrelation, seasonality, and long-term trend, rainfall, LST, RH, and MEI with lags of 3-5 months were associated with HFRS in both study areas. The final model had good accuracy in forecasting the occurrence of HFRS.

CONCLUSIONS

Climate variability plays a significant role in HFRS transmission in northeastern China. The model developed in this study has implications for HFRS control and prevention.

Comparison of three time-series models for predicting campylobacteriosis risk

Three time-series models (regression, decomposition, and Box-Jenkins autoregressive integrated moving averages) were applied to national surveillance data for campylobacteriosis with the goal of disease forecasting in three US states. Datasets spanned 1998-2007 for Minnesota and Oregon, and 1999-2007 for Georgia. Year 2008 was used to validate model results. Mean absolute percent error, mean square error and coefficient of determination (R2) were the main evaluation fit statistics. Results showed that decomposition best captured the temporal patterns in disease risk. Training dataset R2 values were 72.2%, 76.3% and 89.9% and validation year R2 values were 66.2%, 52.6% and 79.9% respectively for Georgia, Oregon and Minnesota. All three techniques could be utilized to predict monthly risk of infection for Campylobacter sp. However, the decomposition model provided the fastest, most accurate, user-friendly method. Use of this model can assist public health personnel in predicting epidemics and developing disease intervention strategies.

Climate variations and Salmonella infection in Australian subtropical and tropical regions

This study aims to quantify the relationship between climate variations and cases of Salmonella infection in subtropical and tropical areas in Australia. Brisbane in a subtropical area and Townsville in a tropical area of Queensland were selected as the study regions. Local meteorological variables and notified cases of Salmonella infection from January 1990 to July 2005 were provided by local authorities. Spearman correlation and time-series adjusted Poisson regression were applied controlling for autoregression, lag effects, seasonal variation and long-term trend. Natural cubic spline and Hockey Stick model were used to estimate a potential threshold temperature. Spearman correlation indicated that maximum and minimum temperatures, relative humidity at 9 am and 3 pm, and rainfall were all positively correlated with the number of cases in both Brisbane and Townsville, with the lag values of the effects up to 2 weeks in Brisbane and 2 months in Townsville. Only temperature and rainfall were significantly included in the regression models in both regions. The models suggested that a potential 1 degrees C rise in maximum or minimum temperature may cause a very similar increase in the number of cases in the two regions. No threshold for the effect of maximum or minimum temperature on Salmonella infection was detected in either region. The association between climate variations and Salmonella infection could be very similar in subtropical and tropical regions in Australia. Temperature and rainfall may be used as key meteorological predictors for the number of cases in both regions.

Climate change and infectious diseases in Europe

Concerted action is needed to address public health issues raised by climate change. In this Review we discuss infections acquired through various routes (arthropod vector, rodent, water, food, and air) in view of a changing climate in Europe. Based on an extensive review of published work and expert workshops, we present an assessment of the infectious disease challenges: incidence, prevalence, and distribution are projected to shift in a changing environment. Due to the high level of uncertainty on the rate of climate change and its impact on infectious diseases, we propose to mount a proactive public health response by building an integrated network for environmental and epidemiological data. This network would have the capacity to connect epidemic intelligence and infectious disease surveillance with meteorological, entomological, water quality, remote sensing, and other data, for multivariate analyses and predictions. Insights from these analyses could then guide adaptation strategies and protect population health from impending threats related to climate change.