Impact of El Niño Southern Oscillation on infectious disease hospitalization risk in the United States

Maternal preconceptional and prenatal exposure to El Niño Southern Oscillation levels and child mortality: a multi-country study

El Niño Southern Oscillation (ENSO) has been shown to relate to the epidemiology of childhood infectious diseases, but evidence for whether they increase child deaths is limited. Here, we investigate the impact of mothers' ENSO exposure during and prior to delivery on child mortality by constructing a retrospective cohort study in 38 low- and middle-income countries. We find that high levels of ENSO indices cumulated over 0-12 lagged months before delivery are associated with significant increases in risks of under-five mortality; with the hazard ratio ranging from 1.33 (95% confidence interval [CI], 1.26, 1.40) to 1.89 (95% CI, 1.78, 2.00). Child mortality risks are particularly related to maternal exposure to El Niño-like conditions in the 0th-1st and 6th-12th lagged months. The El Niño effects are larger in rural populations and those with unsafe sources of drinking water and less education. Thus, preventive interventions are particularly warranted for the socio-economically disadvantaged.

Impacts of climate change on water-related mosquito-borne diseases in temperate regions: A systematic review of literature and meta-analysis

Mosquito-borne diseases are a known tropical phenomenon. This review was conducted to assess the mecha-nisms through which climate change impacts mosquito-borne diseases in temperate regions. Articles were searched from PubMed, Scopus, Web of Science, and Embase databases. Identification criteria were scope (climate change and mosquito-borne diseases), region (temperate), article type (peer-reviewed), publication language (English), and publication years (since 2015). The WWH (who, what, how) framework was applied to develop the research question and thematic analyses identified the mechanisms through which climate change affects mosquito-borne diseases. While temperature ranges for disease transmission vary per mosquito species, all are viable for temperate regions, particularly given projected temperature increases. Zika, chikungunya, and dengue transmission occurs between 18-34 °C (peak at 26-29 °C). West Nile virus establishment occurs at monthly average temperatures between 14-34.3 °C (peak at 23.7-25 °C). Malaria establishment occurs when the consecutive average daily temperatures are above 16 °C until the sum is above 210 °C. The identified mechanisms through which climate change affects the transmission of mosquito-borne diseases in temperate regions include: changes in the development of vectors and pathogens; changes in mosquito habitats; extended transmission seasons; changes in geographic spread; changes in abundance and behaviors of hosts; reduced abundance of mosquito predators; interruptions to control operations; and influence on other non-climate factors. Process and stochastic approaches as well as dynamic and spatial models exist to predict mosquito population dynamics, disease transmission, and climate favorability. Future projections based on the observed relations between climate factors and mosquito-borne diseases suggest that mosquito-borne disease expansion is likely to occur in temperate regions due to climate change. While West Nile virus is already established in some temperate regions, Zika, dengue, chikungunya, and malaria are also likely to become established over time. Moving forward, more research is required to model future risks by incorporating climate, environmental, sociodemographic, and mosquito-related factors under changing climates.

Associations of climate variability driven by El Niño-southern oscillation with excess mortality and related medical costs in Chinese elderly

Climate variability driven by El Niño-Southern Oscillation (ENSO) is a significant public health concern in parallel with global population aging; however, its role in healthy aging is less studied. We examined the longitudinal impacts of ENSO exposure on excess mortality and related medical costs in the elderly from 23 provinces of China. A total of 27,533 non-accidental all-cause deaths were recorded in 30,763 participants during 1998-2018. We found that both low and high levels of ENSO metrics over lags of 0-12 months were associated with increased mortality risks. Specifically, comparing the 10th percentile (-1.8) and 90th percentile (2.0) multivariate El Niño index (MEI) levels to the reference level with the minimum effect of MEI exposure, the risk of mortality was 1.87 (95 % confidence interval [CI], 1.75, 2.00) and 4.89 (95 % CI, 4.36, 5.49), respectively. ENSO exposure was also positively related to medical costs. Further, the associations were stronger among drinkers, lower-income participants, and those with higher blood pressure and heart rate measured at the most recent follow-ups. Our results suggested that ENSO exposure was capable of heightening mortality risks and medical burden among older elderly adults, highlighting that climate variability driven by ENSO could be a crucial determinant of healthy aging.

Communicating ocean and human health connections: An agenda for research and practice

The emergence of ocean and human health (OHH) science as a distinct scholarly discipline has led to increased research outputs from experts in both the natural and social sciences. Formal research on communication strategies, messaging, and campaigns related to OHH science remains limited despite its importance as part of the social processes that can make knowledge actionable. When utilized to communicate visible, local issues for targeting audiences, OHH themes hold the potential to motivate action in pursuit of solutions to environmental challenges, supplementing efforts to address large-scale, abstract, or politicized issues such as ocean acidification or climate change. Probing peer-reviewed literature from relevant areas of study, this review article outlines and reveals associations between society and the quality of coastal and marine ecosystems, as well as key themes, concepts, and findings in OHH science and environmental communication. Recommendations for future work concerning effective ocean and human health science communication are provided, creating a platform for innovative scholarship, evidence-based practice, and novel collaboration across disciplines.

How do El Niño Southern Oscillation (ENSO) and local meteorological factors affect the incidence of seasonal influenza in New York state

Background

Research is lacking in examining how multiple climate factors affect the incidence of seasonal influenza. We investigated the associations between El Niño Southern Oscillation (ENSO), meteorological factors, and influenza incidence in New York State, United States.

Method

We collected emergency department visit data for influenza from the New York State Department of Health. ENSO index was obtained from the National Oceanic and Atmospheric Administration. Meteorological factors, Google Flu Search Index (GFI), and Influenza-like illness (ILI) data in New York State were also collected. Wavelet analysis was used to quantitatively estimate the coherence and phase difference of ENSO, temperature, precipitation, relative humidity, and absolute humidity with emergency department visits of influenza in New York State. Generalized additive models (GAM) were employed to examine the exposure-response relationships between ENSO, weather, and influenza. GFI and ILI data were used to simulate synchronous influenza visits.

Results

The influenza epidemic in New York State had multiple periodic and was primarily on the 1-year scale. The incidence of influenza closely followed the low ENSO index by an average of two months, and the lag period of ENSO on influenza was shorter during 2015-2018. Low temperature in the previous 2 weeks and low absolute humidity in the prior week were positively associated with influenza incidence in New York State. We found an l-shaped association between ENSO index and influenza, a parabolic relationship between temperature in the previous two weeks and influenza, and a linear negative association between absolute humidity in the previous week and influenza. The simulation models including GFI and ILI had higher accuracy for influenza visit estimation.

Conclusions

Low ENSO index, low temperature, and low absolute humidity may drive the influenza epidemics in New York State. The findings can help us deepen the understanding of the climate-influenza association, and help to develop an influenza forecasting model.

Climate Change Drives the Transmission and Spread of Vector-Borne Diseases: An Ecological Perspective

Climate change affects ecosystems and human health in multiple dimensions. With the acceleration of climate change, climate-sensitive vector-borne diseases (VBDs) pose an increasing threat to public health. This paper summaries 10 publications on the impacts of climate change on ecosystems and human health; then it synthesizes the other existing literature to more broadly explain how climate change drives the transmission and spread of VBDs through an ecological perspective. We highlight the multi-dimensional nature of climate change, its interaction with other factors, and the impact of the COVID-19 pandemic on transmission and spread of VBDs, specifically including: (1) the generally nonlinear relationship of local climate (temperature, precipitation and wind) and VBD transmission, with temperature especially exhibiting an n-shape relation; (2) the time-lagged effect of regional climate phenomena (the El Niño-Southern Oscillation and North Atlantic Oscillation) on VBD transmission; (3) the u-shaped effect of extreme climate (heat waves, cold waves, floods, and droughts) on VBD spread; (4) how interactions between non-climatic (land use and human mobility) and climatic factors increase VBD transmission and spread; and (5) that the impact of the COVID-19 pandemic on climate change is debatable, and its impact on VBDs remains uncertain. By exploring the influence of climate change and non-climatic factors on VBD transmission and spread, this paper provides scientific understanding and guidance for their effective prevention and control.

Climate variability and change are drivers of salmonellosis in Australia: 1991 to 2019

Salmonellosis is a climate-sensitive gastroenteritis with over 92 million cases and over 50,000 deaths a year globally. Australia has high rates of salmonellosis compared with other industrialised nations. This study used a negative binomial time-series regression model to investigate the association between Australian salmonellosis notifications and monthly climate variables including El Niño Southern Oscillation (ENSO) and mean temperature anomaly from 1991 to 2019. Between 1991 and 2019 in Australia there were 275,753 salmonellosis notifications and the median annual rate for salmonellosis was 40.1 per 100,000 population. Salmonellosis notifications exhibited strong seasonality, reaching a peak in summer and a minimum in winter. There was an estimated increase of 3.4 % in salmonellosis cases nationally per 1 °C increase in monthly mean temperature anomaly (incidence rate ratio [IRR] of 1.034, 95 % confidence interval [CI]: 1.009, 1.059). Similar associations between salmonellosis and mean temperature anomaly were found for some states. Mean temperature anomaly exhibited an upward trend of 0.9 °C over the period 1991 to 2019. Additionally, a positive association was found between salmonellosis in Australia and ENSO whereby El Niño periods were associated with 7.9 % more salmonellosis cases compared to neutral periods (IRR 1.079, 95 % CI: 1.019, 1.143). A similar ENSO association was detected in the two eastern states of New South Wales and Queensland. This study suggests public health preventative measures to reduce salmonellosis could be enhanced in some regions during El Niño as well as during times of increased temperatures.

Wastewater, waste, and water-based epidemiology (WWW-BE): A novel hypothesis and decision-support tool to unravel COVID-19 in low-income settings?

Traditional wastewater-based epidemiology (W-BE) relying on SARS-CoV-2 RNA detection in wastewater is attractive for understanding COVID-19. Yet traditional W-BE based on centralized wastewaters excludes putative SARS-CoV-2 reservoirs such as: (i) wastewaters from shared on-site sanitation facilities, (ii) solid waste including faecal sludge from non-flushing on-site sanitation systems, and COVID-19 personal protective equipment (PPE), (iii) raw/untreated water, and (iv) drinking water supply systems in low-income countries (LICs). A novel hypothesis and decision-support tool based on Wastewater (on-site sanitation, municipal sewer systems), solid Waste, and raw/untreated and drinking Water-based epidemiology (WWW-BE) is proposed for understanding COVID-19 in LICs. The WWW-BE conceptual framework, including components and principles is presented. Evidence on the presence of SARS-CoV-2 and its proxies in wastewaters, solid materials/waste (papers, metals, fabric, plastics), and raw/untreated surface water, groundwater and drinking water is discussed. Taken together, wastewaters from municipal sewer and on-site sanitation systems, solid waste such as faecal sludge and COVID-19 PPE, raw/untreated surface water and groundwater, and drinking water systems in LICs act as potential reservoirs that receive and harbour SARS-CoV-2, and then transmit it to humans. Hence, WWW-BE could serve a dual function in estimating the prevalence and potential transmission of COVID-19. Several applications of WWW-BE as a hypothesis and decision support tool in LICs are discussed. WWW-BE aggregates data from various infected persons in a spatial unit, hence, putatively requires less resources (analytical kits, personnel) than individual diagnostic testing, making it an ideal decision-support tool for LICs. The novelty, and a critique of WWW-BE versus traditional W-BE are presented. Potential challenges of WWW-BE include: (i) biohazards and biosafety risks, (ii) lack of expertise, analytical equipment, and accredited laboratories, and (iii) high uncertainties in estimates of COVID-19 cases. Future perspectives and research directions including key knowledge gaps and the application of novel and emerging technologies in WWW-BE are discussed.

Global COVID-19 Transmission and Mortality-Influence of Human Development, Climate, and Climate Variability on Early Phase of the Pandemic

Many of the respiratory pathogens show seasonal patterns and association with environmental factors. In this article, we conducted a cross-sectional analysis of the influence of environmental factors, including climate variability, along with development indicators on the differential global spread and fatality of COVID-19 during its early phase. Global climate data we used are monthly averaged gridded data sets of temperature, humidity and temperature anomaly. We used Human Development Index (HDI) to account for all nation wise socioeconomic factors that can affect the reporting of cases and deaths and build a stepwise negative binomial regression model. In the absence of a development indicator, all environmental variables excluding the specific humidity have a significant association with the spread and mortality of COVID-19. Temperature has a weak negative association with COVID-19 mortality. However, HDI is shown to confound the effect of temperature on the reporting of the disease. Temperature anomaly, which is being regarded as a global warming indicator, is positively associated with the pandemic's spread and mortality. Viewing newer infectious diseases like SARS-CoV-2 from the perspective of climate variability has a lot of public health implications, and it necessitates further research.

The temporal characteristics of the lag-response relationship and related key time points between ambient temperature and hand, foot and mouth disease: A multicity study from mainland China

BACKGROUND

Previous studies have thoroughly elucidated the exposure-response relationship between ambient temperature and hand, foot, and mouth disease (HFMD), whereas very little concern has been to the lag-response relationship and related key time points.

OBJECTIVES

We aimed to clarify the temporal characteristics of the lag-response relationship between ambient temperature and HFMD and how they may vary spatially.

METHODS

We retrieved the daily time series of meteorological variables and HFMD counts for 143 cities in mainland China between 2009 and 2014. We estimated the city-specific lag-response curve between ambient temperature and HFMD and related key time points by applying common distributed lag nonlinear models (DLNM) and Monte Carlo simulation methods. Then, we pooled the city-specific estimates by performing a meta-regression with the city-specific characteristics as meta-predictors to explain the potential spatial heterogeneity.

RESULTS

We found a robust lag pattern between temperature and HFMD for different levels of temperatures. The temporal change of risk obtained its maximum value on the current day but dropped sharply thereafter and then rebounded to a secondary peak, which implied the presence of a harvesting effect. By contrast, the estimation of key time points showed substantial heterogeneity, especially at high temperature (the I² statistics ranged from 47% to 80%). With one unit increase in the geographic index, the secondary peak would arrive 0.37 (0.02, 0.71) days later. With one unit increase in the economic index and climatic index, the duration time of the lag-response curve would be lengthened by 0.36 (0.1, 0.62) and 0.92 (0.54, 1.29) days, respectively.

CONCLUSION

Our study examined the lag pattern and spatial heterogeneity of the lag-response relationship between temperature and HFMD. Those findings gave us new insights into the complex association and the related mechanisms between weather and HFMD and important information for weather-based disease early warning systems.

Health Risks Due To Climate Change: Inequity In Causes And Consequences

Climate change has altered global to local weather patterns and increased sea levels, and it will continue to do so. Average temperatures, precipitation amounts, and other variables such as humidity levels are all rising. In addition, weather variability is increasing, causing, for example, a greater number of heat waves, many of which are more intense and last longer, and more floods and droughts. These changes are collectively increasing the number of injuries, illnesses, and deaths from a wide range of climate-sensitive health outcomes. Future health risks will be determined not just by the hazards created by a changing climate but also by the sensitivity of individuals and communities exposed to these hazards and the capacity of health systems to prepare for and effectively manage the attendant risks. These risks include deaths and injuries from extreme events (for example, heat waves, storms, and floods), infectious diseases (including food-, water-, and vectorborne illnesses), and food and water insecurity. These risks are unevenly distributed and both create new inequities and exacerbate those that already exist. Most of these risks are projected to increase with each additional unit of warming. Using an equity lens to move beyond incremental to transformational resilience would reduce vulnerability and improve sustainability for all, but substantial additional funding is required for proactive and effective actions by the health system.

Impact of the Southern Oscillation Index, Temperature, and Precipitation on Eastern Equine Encephalitis Virus Activity in Florida

Eastern equine encephalitis virus (EEEV), an Alphavirus from family Togaviridae, is a highly pathogenic arbovirus affecting the eastern United States, especially Florida. Effects of the Southern Oscillation Index (SOI), precipitation, and cooling degree days on EEEV horse case data in Florida from 2004 to 2018 were modeled using distributed lag nonlinear models (DLNMs). The analysis was conducted at statewide and regional scales. DLNMs were used to model potential delayed effects of the covariates on monthly counts of horse cases. Both models confirmed a seasonal trend in EEEV transmission and found that precipitation, cooling degree days, and the SOI were all predictors of monthly numbers of horse cases. EEEV activity in horses was associated with higher amounts of rainfall during the month of transmission at the statewide scale, as well as the prior 3 mo at the regional scale, fewer cooling degree days during the month of transmission and the preceding 3 mo and high SOI values during the month and the previous 2 mo, and SOI values in the prior 2 to 8 mo. Horse cases were lower during El Niño winters but higher during the following summer, while La Niña winters were associated with higher numbers of cases and fewer during the following summer. At the regional scale, extremely low levels of precipitation were associated with a suppression of EEEV cases for 3 mo. Given the periodicity and potential predictability of El Niño Southern Oscillation (ENSO) cycles, precipitation, and temperature, these results may provide a method for predicting EEEV risk potential in Florida.

Effects of large-scale oceanic phenomena on non-cholera vibriosis incidence in the United States: implications for climate change

Non-cholera Vibrio (NCV) species are important causes of disease. These pathogens are thermophilic and climate change could increase the risk of NCV infection. The El Niño Southern Oscillation (ENSO) is a 'natural experiment' that may presage ocean warming effects on disease incidence. In order to evaluate possible climatic contributions to observed increases in NCV infection, we obtained NCV case counts for the United States from publicly available surveillance data. Trends and impacts of large-scale oceanic phenomena, including ENSO, were evaluated using negative binomial and distributed non-linear lag models (DNLM). Associations between latitude and changing risk were evaluated with meta-regression. Trend models demonstrated expected seasonality (P < 0.001) and a 7% (6.1%-8.1%) annual increase in incidence from 1999 to 2014. DNLM demonstrated increased vibriosis risk following ENSO conditions over the subsequent 12 months (relative risk 1.940, 95% confidence interval (CI) 1.298-2.901). The 'relative-relative risk' (RRR) of annual disease incidence increased with latitude (RRR per 10° increase 1.066, 95% CI 1.027-1.107). We conclude that NCV risk in the United States is impacted by ocean warming, which is likely to intensify with climate change, increasing NCV risk in vulnerable populations.

The impacts of Extra-tropical ENSO Precursors on Tropical Pacific Decadal-scale Variability

Off-equatorial wind anomalies on seasonal timescales from both the North and South Pacific, known as “precursors” of the El Niño Southern Oscillation (ENSO), have been shown to independently trigger the ENSO feedbacks in the tropics and its teleconnections to the extra-tropics. However, the impacts of ENSO precursors on Tropical Pacific Decadal-scale Variability (TPDV) is still not well understood and quantified. We show that the dynamic sequence from extra-tropical ENSO precursors to ENSO (tropics) to extra-tropical ENSO teleconnections is not only important for ENSO, but acts as a primary mechanism to filter (e.g. reddening) the low-frequency variability of the seasonal precursors into the decadal-scale variance of the Pacific basin, accounting for the largest fraction of the TPDV (~65%) and its phase. This process, which contrasts previous theories advocating for a TPDV generated internally in the tropics (e.g. ENSO residuals), is inherently unpredictable and not well reproduced in climate models and raises challenges for understanding and predicting the role of internal TPDV in future climate change scenarios.

Pathogen-Specific Impacts of the 2011-2012 La Niña-Associated Floods on Enteric Infections in the MAL-ED Peru Cohort: A Comparative Interrupted Time Series Analysis

Extreme floods pose multiple direct and indirect health risks. These risks include contamination of water, food, and the environment, often causing outbreaks of diarrheal disease. Evidence regarding the effects of flooding on individual diarrhea-causing pathogens is limited, but is urgently needed in order to plan and implement interventions and prioritize resources before climate-related disasters strike. This study applied a causal inference approach to data from a multisite study that deployed broadly inclusive diagnostics for numerous high-burden common enteropathogens. Relative risks (RRs) of infection with each pathogen during a flooding disaster that occurred at one of the sites-Loreto, Peru-were calculated from generalized linear models using a comparative interrupted time series framework with the other sites as a comparison group and adjusting for background seasonality. During the early period of the flood, increased risk of heat-stable enterotoxigenic E. coli (ST-ETEC) was identified (RR = 1.73 [1.10, 2.71]) along with a decreased risk of enteric adenovirus (RR = 0.36 [0.23, 0.58]). During the later period of the flood, sharp increases in the risk of rotavirus (RR = 5.30 [2.70, 10.40]) and sapovirus (RR = 2.47 [1.79, 3.41]) were observed, in addition to increases in transmission of Shigella spp. (RR = 2.86 [1.81, 4.52]) and Campylobacter spp. (RR = 1.41 (1.01, 1.07). Genotype-specific exploratory analysis reveals that the rise in rotavirus transmission during the flood was likely due to the introduction of a locally atypical, non-vaccine (G2P[4]) strain of the virus. Policy-makers should target interventions towards these pathogens-including vaccines as they become available-in settings where vulnerability to flooding is high as part of disaster preparedness strategies, while investments in radical, transformative, community-wide, and locally-tailored water and sanitation interventions are also needed.

Time-Series Study of Associations between Rates of People Affected by Disasters and the El Niño Southern Oscillation (ENSO) Cycle

The El Niño Southern Oscillation (ENSO) is a major driver of climatic variability that can have far reaching consequences for public health globally. We explored whether global, regional and country-level rates of people affected by natural disasters (PAD) are linked to ENSO. Annual numbers of PAD between 1964–2017 recorded on the EM-DAT disaster database were combined with UN population data to create PAD rates. Time-series regression was used to assess de-trended associations between PAD and 2 ENSO indices: Oceanic Niño Index (ONI) and multivariate El Niño Index (MEI). Over 95% of PAD were caused by floods, droughts or storms, with over 75% of people affected by these three disasters residing in Asia. Globally, drought-related PAD rate increased sharply in El Niño years (versus neutral years). Flood events were the disaster type most strongly associated with El Niño regionally: in South Asia, flood-related PAD increased by 40.5% (95% CI 19.3% to 65.6%) for each boundary point increase in ONI (p = 0.002). India was found to be the country with the largest increase in flood-related PAD rates following an El Niño event, with the Philippines experiencing the largest increase following La Niña. Multivariate ENSO Index (MEI)-analyses showed consistent results. These findings can be used to inform disaster preparedness strategies.

Increasing Babesiosis in Southeastern Pennsylvania, 2008-2017

Tick-borne illnesses are increasing but are often underreported. Few cases of babesiosis have been reported from Pennsylvania. Our 4-hospital system in southeastern Pennsylvania saw a rise in cases from 7 or fewer yearly in 2008–2014 to 26 cases in 2015. There appear to be multiple potential causes of this increase in frequency.

Modeling the Present and Future Incidence of Pediatric Hand, Foot, and Mouth Disease Associated with Ambient Temperature in Mainland China

BACKGROUND

There is limited evidence about the association between ambient temperature and the incidence of pediatric hand, foot, and mouth disease (HFMD) nationwide in China.

OBJECTIVES

We examined the childhood temperature-HFMD associations across mainland China, and we projected the change in HFMD cases due to projected temperature change by the 2090s.

METHODS

Data on daily HFMD (children 0-14 y old) counts and weather were collected from 362 sites during 2009-2014. Daily temperature by the 2090s was downscaled under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. Temperature-HFMD associations were quantified using a two-stage Poisson regression with a distributed lag nonlinear model. The impact of changes in temperature on the incidence of HFMD was estimated by combining the fitted temperature-HFMD associations with projected temperatures under each scenario, assuming a constant population structure. Sensitivity analyses were performed to assess the influence of primary model assumptions.

RESULTS

During 2009-2014, >11 million HFMD cases were reported. In most regions, the temperature-HFMD association had an inverted U shape with a peak at approximately 20°C, but the association leveled off or continued to increase in the Inner Mongolia and Northeast regions. When estimates were pooled across all regions and the population size was held constant, the projected incidence of HFMD increased by 3.2% [95% empirical confidence interval (eCI): −13.5%, 20.0%] and 5.3% (95% eCI: −33.3%, 44.0%) by the 2090s under the RCP 4.5 and 8.5 scenarios, respectively. However, regional projections suggest that HFMD may decrease with climate change in temperate areas of central and eastern China.

CONCLUSION

Our estimates suggest that the association between temperature and HFMD varies across China and that the future impact of climate change on HFMD incidence will vary as well. Other factors, including changes in the size of the population at risk (children 0-14 y old) will also influence future HFMD trends. https://doi.org/10.1289/EHP3062.

Identifying climate drivers of infectious disease dynamics: recent advances and challenges ahead

Climate change is likely to profoundly modulate the burden of infectious diseases. However, attributing health impacts to a changing climate requires being able to associate changes in infectious disease incidence with the potentially complex influences of climate. This aim is further complicated by nonlinear feedbacks inherent in the dynamics of many infections, driven by the processes of immunity and transmission. Here, we detail the mechanisms by which climate drivers can shape infectious disease incidence, from direct effects on vector life history to indirect effects on human susceptibility, and detail the scope of variation available with which to probe these mechanisms. We review approaches used to evaluate and quantify associations between climate and infectious disease incidence, discuss the array of data available to tackle this question, and detail remaining challenges in understanding the implications of climate change for infectious disease incidence. We point to areas where synthesis between approaches used in climate science and infectious disease biology provide potential for progress.

Climate Variability, Vulnerability, and Natural Disasters: A Case Study of Zika Virus in Manabi, Ecuador Following the 2016 Earthquake

Climate change presents complex and wide-reaching threats to human health. A variable and changing climate can amplify and unmask ecological and socio-political weaknesses and increase the risk of adverse health outcomes in socially vulnerable regions. When natural disasters occur in such areas, underlying climatic conditions may amplify the public health crisis. We describe an emerging epidemic of Zika virus (ZIKV) in Ecuador following the 2016 earthquake, which coincided with an exceptionally strong El Niño event. We hypothesize that the trigger of a natural disaster during anomalous climate conditions and underlying social vulnerabilities were force multipliers contributing to a dramatic increase in ZIKV cases postearthquake.

Effect of Weather Variability on Seasonal Influenza Among Different Age Groups in Queensland, Australia: A Bayesian Spatiotemporal Analysis

Background

The effects of weather variability on seasonal influenza among different age groups remain unclear. The comparative study aims to explore the differences in the associations between weather variability and seasonal influenza, and growth rates of seasonal influenza epidemics among different age groups in Queensland, Australia.

Methods

Three Bayesian spatiotemporal conditional autoregressive models were fitted at the postal area level to quantify the relationships between seasonal influenza and monthly minimum temperature (MIT), monthly vapor pressure, school calendar pattern, and Index of Relative Socio-Economic Advantage and Disadvantage for 3 age groups (<15, 15-64, and ≥65 years).

Results

The results showed that the expected decrease in monthly influenza cases was 19.3% (95% credible interval [CI], 14.7%-23.4%), 16.3% (95% CI, 13.6%-19.0%), and 8.5% (95% CI, 1.5%-15.0%) for a 1°C increase in monthly MIT at <15, 15-64, and ≥65 years of age, respectively, while the average increase in the monthly influenza cases was 14.6% (95% CI, 9.0%-21.0%), 12.1% (95% CI, 8.8%-16.1%), and 9.2% (95% CI, 1.4%-16.9%) for a 1-hPa increase in vapor pressure.

Conclusions

Weather variability appears to be more influential on seasonal influenza transmission in younger (0-14) age groups. The growth rates of influenza at postal area level were relatively small for older (≥65) age groups in Queensland, Australia.