A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk.

BACKGROUND: Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. METHODS: We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.). RESULTS: We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures. CONCLUSIONS: Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping.

Tracking the impacts of climate change on human health via indicators: lessons from the Lancet Countdown.

BACKGROUND: In the past decades, climate change has been impacting human lives and health via extreme weather and climate events and alterations in labour capacity, food security, and the prevalence and geographical distribution of infectious diseases across the globe. Climate change and health indicators (CCHIs) are workable tools designed to capture the complex set of interdependent interactions through which climate change is affecting human health. Since 2015, a novel sub-set of CCHIs, focusing on climate change impacts, exposures, and vulnerability indicators (CCIEVIs) has been developed, refined, and integrated by Working Group 1 of the "Lancet Countdown: Tracking Progress on Health and Climate Change", an international collaboration across disciplines that include climate, geography, epidemiology, occupation health, and economics. DISCUSSION: This research in practice article is a reflective narrative documenting how we have developed CCIEVIs as a discrete set of quantifiable indicators that are updated annually to provide the most recent picture of climate change's impacts on human health. In our experience, the main challenge was to define globally relevant indicators that also have local relevance and as such can support decision making across multiple spatial scales. We found a hazard, exposure, and vulnerability framework to be effective in this regard. We here describe how we used such a framework to define CCIEVIs based on both data availability and the indicators' relevance to climate change and human health. We also report on how CCIEVIs have been improved and added to, detailing the underlying data and methods, and in doing so provide the defining quality criteria for Lancet Countdown CCIEVIs. CONCLUSIONS: Our experience shows that CCIEVIs can effectively contribute to a world-wide monitoring system that aims to track, communicate, and harness evidence on climate-induced health impacts towards effective intervention strategies. An ongoing challenge is how to improve CCIEVIs so that the description of the linkages between climate change and human health can become more and more comprehensive.

COVID-19 research priorities for non-pharmaceutical public health and social measures.

Europe is in the midst of a COVID-19 epidemic and a number of non-pharmaceutical public health and social measures have been implemented, in order to contain the transmission of severe acute respiratory syndrome coronavirus 2. These measures are fundamental elements of the public health approach to controlling transmission but have proven not to be sufficiently effective. Therefore, the European Centre for Disease Prevention and Control has conducted an assessment of research gaps that can help inform policy decisions regarding the COVID-19 response. We have identified research gaps in the area of non-pharmaceutical measures, physical distancing, contact tracing, transmission, communication, mental health, seasonality and environment/climate, surveillance and behavioural aspects of COVID-19. This prioritisation exercise is a step towards the global efforts of developing a coherent research road map in coping with the current epidemic but also developing preparedness measures for the next unexpected epidemic.

Reviewing estimates of the basic reproduction number for dengue, Zika and chikungunya across global climate zones.

BACKGROUND: Globally, dengue, Zika virus, and chikungunya are important viral mosquito-borne diseases that infect millions of people annually. Their geographic range includes not only tropical areas but also sub-tropical and temperate zones such as Japan and Italy. The relative severity of these arboviral disease outbreaks can vary depending on the setting. In this study we explore variation in the epidemiologic potential of outbreaks amongst these climatic zones and arboviruses in order to elucidate potential reasons behind such differences. METHODOLOGY: We reviewed the peer-reviewed literature (PubMed) to obtain basic reproduction number (R0) estimates for dengue, Zika virus, and chikungunya from tropical, sub-tropical and temperate regions. We also computed R0 estimates for temperate and sub-tropical climate zones, based on the outbreak curves in the initial outbreak phase. Lastly we compared these estimates across climate zones, defined by latitude. RESULTS: Of 2115 studies, we reviewed the full text of 128 studies and included 65 studies in our analysis. Our results suggest that the R0 of an arboviral outbreak depends on climate zone, with lower R0 estimates, on average, in temperate zones (R0 = 2.03) compared to tropical (R0 = 3.44) and sub-tropical zones (R0 = 10.29). The variation in R0 was considerable, ranging from 0.16 to 65. The largest R0 was for dengue (65) and was estimated by the Ross-Macdonald model in the tropical zone, whereas the smallest R0 (0.16) was for Zika virus and was estimated statistically from an outbreak curve in the sub-tropical zone. CONCLUSIONS: The results indicate climate zone to be an important determinant of the basic reproduction number, R0, for dengue, Zika virus, and chikungunya. The role of other factors as determinants of R0, such as methods, environmental and social conditions, and disease control, should be further investigated. The results suggest that R0 may increase in temperate regions in response to global warming, and highlight the increasing need for strengthening preparedness and control activities.

Using volunteered geographic information to assess mobility in the early phases of the COVID-19 pandemic: a cross-city time series analysis of 41 cities in 22 countries from March 2nd to 26th 2020.

OBJECTIVES: Restricting mobility is a central aim for lowering contact rates and preventing COVID-19 transmission. Yet the impact on mobility of different non-pharmaceutical countermeasures in the earlier stages of the pandemic is not well-understood. DESIGN: Trends were evaluated using Citymapper's mobility index covering 2nd to 26th March 2020, expressed as percentages of typical usage periods from 0% as the lowest and 100% as normal. China and India were not covered. Multivariate fixed effects models were used to estimate the association of policies restricting movement on mobility before and after their introduction. Policy restrictions were assessed using the Oxford COVID-19 Government Response Stringency Index as well as measures coding the timing and degree of school and workplace closures, transport restrictions, and cancellation of mass gatherings. SETTING: 41 cities worldwide. MAIN OUTCOME MEASURES: Citymapper's mobility index. RESULTS: Mobility declined in all major cities throughout March. Larger declines were seen in European than Asian cities. The COVID-19 Government Response Stringency Index was strongly associated with declines in mobility (r = - 0.75, p < 0.001). After adjusting for time-trends, we observed that implementing non-pharmaceutical countermeasures was associated with a decline of mobility of 10.0% for school closures (95% CI: 4.36 to 15.7%), 15.0% for workplace closures (95% CI: 10.2 to 19.8%), 7.09% for cancelling public events (95% CI: 1.98 to 12.2%), 18.0% for closing public transport (95% CI: 6.74 to 29.2%), 13.3% for restricting internal movements (95% CI: 8.85 to 17.8%) and 5.30% for international travel controls (95% CI: 1.69 to 8.90). In contrast, as expected, there was no association between population mobility changes and fiscal or monetary measures or emergency healthcare investment. CONCLUSIONS: Understanding the effect of public policy on mobility in the early stages is crucial to slowing and reducing COVID-19 transmission. By using Citymapper's mobility index, this work provides the first evidence about trends in mobility and the impacts of different policy interventions, suggesting that closure of public transport, workplaces and schools are particularly impactful.

Can economic indicators predict infectious disease spread? A cross-country panel analysis of 13 European countries.

Aims: It is unclear how economic factors impact on the epidemiology of infectious disease. We evaluated the relationship between incidence of selected infectious diseases and economic factors, including economic downturn, in 13 European countries between 1970 and 2010. Methods: Data were obtained from national communicable disease surveillance centres. Negative binomial forms of the generalised additive model (GAM) and the generalised linear model were tested to see which best reflected transmission dynamics of: diphtheria, pertussis, measles, meningococcal disease, hepatitis B, gonorrhoea, syphilis, hepatitis A and salmonella. Economic indicators were gross domestic product per capita (GDPpc), unemployment rates and (economic) downturn. Results: GAM models produced the best goodness-of-fit results. The relationship between GDPpc and disease incidence was often non-linear. Strength and directions of association between population age, tertiary education levels, GDPpc and unemployment were disease dependent. Overdispersion for almost all diseases validated the assumption of a negative binomial relationship. Downturns were not independently linked to disease incidence. Conclusions: Social and economic factors can be correlated with many infections. However, the trend is not always in the same direction, and these associations are often non-linear. Economic downturn or recessions as indicators of increased disease risk may be better replaced by GDPpc or unemployment measures.

Natural disasters and infectious disease in Europe: a literature review to identify cascading risk pathways.

BACKGROUND: Natural disasters are increasing in their frequency and complexity. Understanding how their cascading effects can lead to infectious disease outbreaks is important for developing cross-sectoral preparedness strategies. The review focussed on earthquakes and floods because of their importance in Europe and their potential to elucidate the pathways through which natural disasters can lead to infectious disease outbreaks. METHODS: A systematic literature review complemented by a call for evidence was conducted to identify earthquake or flooding events in Europe associated with potential infectious disease events. RESULTS: This review included 17 peer-reviewed papers that reported on suspected and confirmed infectious disease outbreaks following earthquakes (4 reports) or flooding (13 reports) in Europe. The majority of reports related to food- and water-borne disease. Eleven studies described the cascading effect of post-disaster outbreaks. The most reported driver of disease outbreaks was heavy rainfall, which led to cross-connections between water and other environmental systems, leading to the contamination of rivers, lakes, springs and water supplies. Exposure to contaminated surface water or floodwater following flooding, exposure to animal excreta and post-disaster living conditions were among other reported drivers of outbreaks. CONCLUSIONS: The cascade effects of natural disasters, such as earthquakes and floods, include outbreaks of infectious disease. The projection that climate change-related extreme weather events will increase in Europe in the coming century highlights the importance of strengthening preparedness planning and measures to mitigate and control outbreaks in post-disaster settings.

Using Big Data to Monitor the Introduction and Spread of Chikungunya, Europe, 2017.

With regard to fully harvesting the potential of big data, public health lags behind other fields. To determine this potential, we applied big data (air passenger volume from international areas with active chikungunya transmission, Twitter data, and vectorial capacity estimates of Aedes albopictus mosquitoes) to the 2017 chikungunya outbreaks in Europe to assess the risks for virus transmission, virus importation, and short-range dispersion from the outbreak foci. We found that indicators based on voluminous and velocious data can help identify virus dispersion from outbreak foci and that vector abundance and vectorial capacity estimates can provide information on local climate suitability for mosquitoborne outbreaks. In contrast, more established indicators based on Wikipedia and Google Trends search strings were less timely. We found that a combination of novel and disparate datasets can be used in real time to prevent and control emerging and reemerging infectious diseases.

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.

Management and control of communicable diseases in schools and other child care settings: systematic review on the incubation period and period of infectiousness.

BACKGROUND: Information on the incubation period and period of infectiousness or shedding of infectious pathogens is critical for management and control of communicable diseases in schools and other childcare settings. METHODS: We performed a systematic literature review (Pubmed and Embase) to identify and critically appraise all relevant published articles using incubation, infectiousness or shedding, and exclusion period as parameters for the search. No language, time, geographical or study design restrictions were applied. RESULTS: A total of 112 articles met the eligibility criteria. A relatively large number were retrieved for gastrointestinal diseases and influenza or respiratory syncytial virus, but there were few or no studies for other diseases. Although a considerable number of publications reported the incubation and shedding periods, there was less evidence concerning the period of infectiousness. On average, five days of exclusion is considered for measles, mumps, rubella, varicella and pertussis. For other diseases, such as most cases of meningococcal disease, hepatitis A and influenza exclusion is considered as long as severe symptoms persist. However, these results are based on a diverse range of study characteristics, including age, treatment, vaccination, underlying diseases, diagnostic tools, viral load, study design and definitions, making statistical analysis difficult. CONCLUSIONS: Despite inconsistent definitions for key variables and the diversity of studies reviewed, published data provide sufficient quantitative estimates to inform decision making in schools and other childcare settings. The results can be used as a reference when deciding about the exclusion of a child with a communicable disease that both prevents exposure and avoids unnecessary absenteeism.

Determinants and Drivers of Infectious Disease Threat Events in Europe.

Infectious disease threat events (IDTEs) are increasing in frequency worldwide. We analyzed underlying drivers of 116 IDTEs detected in Europe during 2008-2013 by epidemic intelligence at the European Centre of Disease Prevention and Control. Seventeen drivers were identified and categorized into 3 groups: globalization and environment, sociodemographic, and public health systems. A combination of >2 drivers was responsible for most IDTEs. The driver category globalization and environment contributed to 61% of individual IDTEs, and the top 5 individual drivers of all IDTEs were travel and tourism, food and water quality, natural environment, global trade, and climate. Hierarchical cluster analysis of all drivers identified travel and tourism as a distinctly separate driver. Monitoring and modeling such disease drivers can help anticipate future IDTEs and strengthen control measures. More important, intervening directly on these underlying drivers can diminish the likelihood of the occurrence of an IDTE and reduce the associated human and economic costs.

Current medical research funding and frameworks are insufficient to address the health risks of global environmental change.

BACKGROUND: Three major international agreements signed in 2015 are key milestones for transitioning to more sustainable and resilient societies: the UN 2030 Agenda for Sustainable Development; the Sendai Framework for Disaster Risk Reduction; and the Paris Agreement under the United Nations Framework Convention on Climate Change. Together, these agreements underscore the critical importance of understanding and managing the health risks of global changes, to ensure continued population health improvements in the face of significant social and environmental change over this century. BODY: Funding priorities of major health institutions and organizations in the U.S. and Europe do not match research investments with needs to inform implementation of these international agreements. In the U.S., the National Institutes of Health commit 0.025 % of their annual research budget to climate change and health. The European Union Seventh Framework Programme committed 0.08 % of the total budget to climate change and health; the amount committed under Horizon 2020 was 0.04 % of the budget. Two issues apparently contributing to this mismatch are viewing climate change primarily as an environmental problem, and therefore the responsibility of other research streams; and narrowly framing research into managing the health risks of climate variability and change from the perspective of medicine and traditional public health. This reductionist, top-down perspective focuses on proximate, individual level risk factors. While highly successful in reducing disease burdens, this framing is insufficient to protect health and well-being over a century that will be characterized by profound social and environmental changes. CONCLUSIONS: International commitments in 2015 underscored the significant challenges societies will face this century from climate change and other global changes. However, the low priority placed on understanding and managing the associated health risks by national and international research institutions and organizations leaves populations poorly prepared to cope with changing health burdens. Risk-centered, systems approaches can facilitate understanding of the complex interactions and dependencies across environmental, social, and human systems. This understanding is needed to formulate effective interventions targeting socio-environmental factors that are as important for determining health burdens as are individual risk factors.

Climate change projections of West Nile virus infections in Europe: implications for blood safety practices.

BACKGROUND: West Nile virus (WNV) is transmitted by mosquitoes in both urban as well as in rural environments and can be pathogenic in birds, horses and humans. Extrinsic factors such as temperature and land use are determinants of WNV outbreaks in Europe, along with intrinsic factors of the vector and virus. METHODS: With a multivariate model for WNV transmission we computed the probability of WNV infection in 2014, with July 2014 temperature anomalies. We applied the July temperature anomalies under the balanced A1B climate change scenario (mix of all energy sources, fossil and non-fossil) for 2025 and 2050 to model and project the risk of WNV infection in the future. Since asymptomatic infections are common in humans (which can result in the contamination of the donated blood) we estimated the predictive prevalence of WNV infections in the blood donor population. RESULTS: External validation of the probability model with 2014 cases indicated good prediction, based on an Area Under Curve (AUC) of 0.871 (SD = 0.032), on the Receiver Operating Characteristic Curve (ROC). The climate change projections for 2025 reveal a higher probability of WNV infection particularly at the edges of the current transmission areas (for example in Eastern Croatia, Northeastern and Northwestern Turkey) and an even further expansion in 2050. The prevalence of infection in (blood donor) populations in the outbreak-affected districts is expected to expand in the future. CONCLUSIONS: Predictive modelling of environmental and climatic drivers of WNV can be a valuable tool for public health practice. It can help delineate districts at risk for future transmission. These areas can be subjected to integrated disease and vector surveillance, outreach to the public and health care providers, implementation of personal protective measures, screening of blood donors, and vector abatement activities.

Association between heavy precipitation events and waterborne outbreaks in four Nordic countries, 1992-2012.

We conducted a matched case-control study to examine the association between heavy precipitation events and waterborne outbreaks (WBOs) by linking epidemiological registries and meteorological data between 1992 and 2012 in four Nordic countries. Heavy precipitation events were defined by above average (exceedance) daily rainfall during the preceding weeks using local references. We performed conditional logistic regression using the four previous years as the controls. Among WBOs with known onset date (n = 89), exceedance rainfall on two or more days was associated with occurrence of outbreak, OR = 3.06 (95% CI 1.38-6.78), compared to zero exceedance days. Stratified analyses revealed a significant association with single household water supplies, ground water as source and for outbreaks occurring during spring and summer. These findings were reproduced in analyses including all WBOs with known outbreak month (n = 186). The vulnerability of single households to WBOs associated with heavy precipitation events should be communicated to homeowners and implemented into future policy planning to reduce the risk of waterborne illness.

Tuberculosis control and economic recession: longitudinal study of data from 21 European countries, 1991-2012.

OBJECTIVE: To investigate whether the economic recession affected the control of tuberculosis in the European Union. METHODS: Multivariate regression models were used to quantify the association between gross domestic product, public health expenditure and tuberculosis case detection rates, using data from 21 European Union member states (1991-2012). The estimated changes in case detection attributable to the recession were combined with mathematical models of tuberculosis transmission, to project the potential influence of the recession on tuberculosis epidemiology until 2030. FINDINGS: Between 1991 and 2007, detection rates for sputum-smear-positive tuberculosis in the European Union were stable at approximately 85%. During the economic recession (2008-2011) detection rates declined by a mean of 5.22% (95% confidence interval, CI: 2.54-7.90) but treatment success rates showed no significant change (P = 0.62). A fall in economic output of 100 United States dollars per capita was associated with a 0.22% (95% CI: 0.05-0.39) mean reduction in the tuberculosis case detection rate. An equivalent fall in spending on public health services was associated with a 2.74% (95% CI: 0.31-5.16) mean reduction in the detection rate. Mathematical models suggest that the recession and consequent austerity policies will lead to increases in tuberculosis prevalence and tuberculosis-attributable mortality that are projected to persist for over a decade. CONCLUSION: Across the European Union, reductions in spending on public health services appear to have reduced tuberculosis case detection and to have increased the long-term risk of a resurgence in the disease.

Analytical studies assessing the association between extreme precipitation or temperature and drinking water-related waterborne infections: a review.

Determining the role of weather in waterborne infections is a priority public health research issue as climate change is predicted to increase the frequency of extreme precipitation and temperature events. To document the current knowledge on this topic, we performed a literature review of analytical research studies that have combined epidemiological and meteorological data in order to analyze associations between extreme precipitation or temperature and waterborne disease.A search of the databases Ovid MEDLINE, EMBASE, SCOPUS and Web of Science was conducted, using search terms related to waterborne infections and precipitation or temperature. Results were limited to studies published in English between January 2001 and December 2013.Twenty-four articles were included in this review, predominantly from Asia and North-America. Four articles used waterborne outbreaks as study units, while the remaining articles used number of cases of waterborne infections. Results presented in the different articles were heterogeneous. Although most of the studies identified a positive association between increased precipitation or temperature and infection, there were several in which this association was not evidenced. A number of articles also identified an association between decreased precipitation and infections. This highlights the complex relationship between precipitation or temperature driven transmission and waterborne disease. We encourage researchers to conduct studies examining potential effect modifiers, such as the specific type of microorganism, geographical region, season, type of water supply, water source or water treatment, in order to assess how they modulate the relationship between heavy rain events or temperature and waterborne disease. Addressing these gaps is of primary importance in order to identify the areas where action is needed to minimize negative impact of climate change on health in the future.

Environmental predictors of West Nile fever risk in Europe.

BACKGROUND: West Nile virus (WNV) is a mosquito-borne pathogen of global public health importance. Transmission of WNV is determined by abiotic and biotic factors. The objective of this study was to examine environmental variables as predictors of WNV risk in Europe and neighboring countries, considering the anomalies of remotely sensed water and vegetation indices and of temperature at the locations of West Nile fever (WNF) outbreaks reported in humans between 2002 and 2013. METHODS: The status of infection by WNV in relationship to environmental and climatic risk factors was analyzed at the district level using logistic regression models. Temperature, remotely sensed Normalized Difference Vegetation Index (NDVI) and Modified Normalized Difference Water Index (MNDWI) anomalies, as well as population, birds' migratory routes, and presence of wetlands were considered as explanatory variables. RESULTS: The anomalies of temperature in July, of MNDWI in early June, the presence of wetlands, the location under migratory routes, and the occurrence of a WNF outbreak the previous year were identified as risk factors. The best statistical model according to the Akaike Information Criterion was used to map WNF risk areas in 2012 and 2013. Model validations showed a good level of prediction: area under Receiver Operator Characteristic curve = 0.854 (95% Confidence Interval 0.850-0.856) for internal validation and 0.819 (95% Confidence Interval 0.814-0.823) (2012) and 0.853 (95% Confidence Interval 0.850-0.855) (2013) for external validations, respectively. CONCLUSIONS: WNF incidence is increasing in Europe and WNV is expanding into new areas where it had never been observed before. Our model can be used to direct surveillance activities and public health interventions for the upcoming WNF season.