Malaria early warnings based on seasonal climate forecasts from multi-model ensembles

Spatial spillovers of violent conflict amplify the impacts of climate variability on malaria risk in sub-Saharan Africa

Africa carries a disproportionately high share of the global malaria burden, accounting for 94% of malaria cases and deaths worldwide in 2019. It is also a politically unstable region and the most vulnerable continent to climate change in recent decades. Knowledge about the modifying impacts of violent conflict on climate-malaria relationships remains limited. Here, we quantify the associations between violent conflict, climate variability, and malaria risk in sub-Saharan Africa using health surveys from 128,326 individuals, historical climate data, and 17,429 recorded violent conflicts from 2006 to 2017. We observe that spatial spillovers of violent conflict (SSVCs) have spatially distant effects on malaria risk. Malaria risk induced by SSVCs within 50 to 100 km from the households gradually increases from 0.1% (not significant, P>0.05) to 6.5% (95% CI: 0 to 13.0%). SSVCs significantly promote malaria risk within the average 20.1 to 26.9 °C range. At the 12-mo mean temperature of 22.5 °C, conflict deaths have the largest impact on malaria risk, with an approximately 5.8% increase (95% CI: 1.0 to 11.0%). Additionally, a pronounced association between SSVCs and malaria risk exists in the regions with 9.2 wet days per month. The results reveal that SSVCs increase population exposure to harsh environments, amplifying the effect of warm temperature and persistent precipitation on malaria transmission. Violent conflict therefore poses a substantial barrier to mosquito control and malaria elimination efforts in sub-Saharan Africa. Our findings support effective targeting of treatment programs and vector control activities in conflict-affected regions with a high malaria risk.

Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model

Climatic factors influence malaria transmission via the effect on the Anopheles vector and Plasmodium parasite. Modelling and understanding the complex effects that climate has on malaria incidence can enable important early warning capabilities. Deep learning applications across fields are proving valuable, however the field of epidemiological forecasting is still in its infancy with a lack of applied deep learning studies for malaria in southern Africa which leverage quality datasets. Using a novel high resolution malaria incidence dataset containing 23 years of daily data from 1998 to 2021, a statistical model and XGBOOST machine learning model were compared to a deep learning Transformer model by assessing the accuracy of their numerical predictions. A novel loss function, used to account for the variable nature of the data yielded performance around + 20% compared to the standard MSE loss. When numerical predictions were converted to alert thresholds to mimic use in a real-world setting, the Transformer's performance of 80% according to AUROC was 20-40% higher than the statistical and XGBOOST models and it had the highest overall accuracy of 98%. The Transformer performed consistently with increased accuracy as more climate variables were used, indicating further potential for this prediction framework to predict malaria incidence at a daily level using climate data for southern Africa.

El Niño and other climatic drivers of epidemic malaria in Ethiopia: new tools for national health adaptation plans

BACKGROUND

Ethiopia has a history of climate related malaria epidemics. An improved understanding of malaria-climate interactions is needed to inform malaria control and national adaptation plans.

METHODS

Malaria-climate associations in Ethiopia were assessed using (a) monthly climate data (1981-2016) from the Ethiopian National Meteorological Agency (NMA), (b) sea surface temperatures (SSTs) from the eastern Pacific, Indian Ocean and Tropical Atlantic and (c) historical malaria epidemic information obtained from the literature. Data analysed spanned 1950-2016. Individual analyses were undertaken over relevant time periods. The impact of the El Niño Southern Oscillation (ENSO) on seasonal and spatial patterns of rainfall and minimum temperature (Tmin) and maximum temperature (Tmax) was explored using NMA online Maprooms. The relationship of historic malaria epidemics (local or widespread) and concurrent ENSO phases (El Niño, Neutral, La Niña) and climate conditions (including drought) was explored in various ways. The relationships between SSTs (ENSO, Indian Ocean Dipole and Tropical Atlantic), rainfall, Tmin, Tmax and malaria epidemics in Amhara region were also explored.

RESULTS

El Niño events are strongly related to higher Tmax across the country, drought in north-west Ethiopia during the July-August-September (JAS) rainy season and unusually heavy rain in the semi-arid south-east during the October-November-December (OND) season. La Niña conditions approximate the reverse. At the national level malaria epidemics mostly occur following the JAS rainy season and widespread epidemics are commonly associated with El Niño events when Tmax is high, and drought is common. In the Amhara region, malaria epidemics were not associated with ENSO, but with warm Tropical Atlantic SSTs and higher rainfall.

CONCLUSION

Malaria-climate relationships in Ethiopia are complex, unravelling them requires good climate and malaria data (as well as data on potential confounders) and an understanding of the regional and local climate system. The development of climate informed early warning systems must, therefore, target a specific region and season when predictability is high and where the climate drivers of malaria are sufficiently well understood. An El Niño event is likely in the coming years. Warming temperatures, political instability in some regions, and declining investments from international donors, implies an increasing risk of climate-related malaria epidemics.

Synergies between environmental degradation and climate variation on malaria re-emergence in southern Venezuela: a spatiotemporal modelling study

BACKGROUND

Environmental degradation facilitates the emergence of vector-borne diseases, such as malaria, through changes in the ecological landscape that increase human-vector contacts and that expand vector habitats. However, the modifying effects of environmental degradation on climate-disease relationships have not been well explored. Here, we investigate the rapid re-emergence of malaria in a transmission hotspot in southern Venezuela and explore the synergistic effects of environmental degradation, specifically gold-mining activity, and climate variation.

METHODS

In this spatiotemporal modelling study of the 46 parishes of the state of Bolívar, southeast Venezuela, we used data from the Venezuelan Ministry of Health including population data and monthly cases of Plasmodium falciparum malaria and Plasmodium vivax malaria between 1996 and 2016. We estimated mean precipitation and temperature using the ERA5-Land dataset and used monthly anomalies in sea-surface temperature as an indicator of El Niño events between 1996 and 2016. The location of suspected mining sites in Bolívar in 2009, 2017, and 2018 were sourced from the Amazon Geo-Referenced Socio-Environmental Information Network. We estimated measures of cumulative forest loss and urban development by km2 using annual land cover maps from the European Space Agency Climate Change Initiative between 1996 and 2016. We modelled monthly cases of P falciparum and P vivax malaria using a Bayesian hierarchical mixed model framework. We quantified the variation explained by mining activity before exploring the modifying effects of environmental degradation on climate-malaria relationships.

FINDINGS

We observed a 27% reduction in the additional unexplained spatial variation in incidence of P falciparum malaria and a 23% reduction in P vivax malaria when mining was included in our models. The effect of temperature on malaria was greater in high mining areas than low mining areas, and the P falciparum malaria effect size at temperatures of 26·5°C (2·4 cases per 1000 people [95% CI 1·78-3·06]) was twice as high as the effect in low mining areas (1 case per 1000 people [0·68-1·49]).

INTERPRETATION

We show that mining activity in southern Venezuela is associated with hotspots of malaria transmission. Increased temperatures exacerbated malaria transmission in mining areas, highlighting the need to consider how environmental degradation modulates climate effect on disease risk, which is especially important in areas subjected to rapidly rising temperatures and land-use change globally. Our findings have implications for the progress towards malaria elimination in the Latin American region. Our findings are also important for effectively targeting timely treatment programmes and vector-control activities in mining areas with high rates of malaria transmission.

FUNDING

Biotechnology and Biological Sciences Research Council, Royal Society, US National Institutes of Health, and Global Challenges Research Fund.

TRANSLATION

For the Spanish translation of the abstract see Supplementary Materials section.

Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning

Malaria is the cause of nearly half a million deaths worldwide each year, posing a great socioeconomic burden. Despite recent progress in understanding the influence of climate on malaria infection rates, climatic sources of predictability remain poorly understood and underexploited. Local weather variability alone provides predictive power at short lead times of 1-2 months, too short to adequately plan intervention measures. Here, we show that tropical climatic variability and associated sea surface temperature over the Pacific and Indian Oceans are valuable for predicting malaria in Limpopo, South Africa, up to three seasons ahead. Climatic precursors of malaria outbreaks are first identified via lag-regression analysis of climate data obtained from reanalysis and observational datasets with respect to the monthly malaria case count data provided from 1998-2020 by the Malaria Institute in Tzaneen, South Africa. Out of 11 sea surface temperature sectors analyzed, two regions, the Indian Ocean and western Pacific Ocean regions, emerge as the most robust precursors. The predictive value of these precursors is demonstrated by training a suite of machine-learning classification models to predict whether malaria case counts are above or below the median historical levels and assessing their skills in providing early warning predictions of malaria incidence with lead times ranging from 1 month to a year. Through the development of this prediction system, we find that past information about SST over the western Pacific Ocean offers impressive prediction skills (~80% accuracy) for up to three seasons (9 months) ahead. SST variability over the tropical Indian Ocean is also found to provide good skills up to two seasons (6 months) ahead. This outcome represents an extension of the effective prediction lead time by about one to two seasons compared to previous prediction systems that were more computationally costly compared to the machine learning techniques used in the current study. It also demonstrates the value of climatic information and the prediction framework developed herein for the early planning of interventions against malaria outbreaks.

Temporal Correlation Between Urban Microclimate, Vector Mosquito Abundance, and Dengue Cases

Dengue Hemorrhagic Fever (DHF) is a major mosquito-borne viral disease. Studies have reported a strong correlation between weather, the abundance of Aedes aegypti, the vector of DHF virus, and dengue incidence. However, this conclusion has been based on the general climate pattern of wide regions. In general, however, the human population, level of infrastructure, and land-use change in rural and urban areas often produce localized climate patterns that may influence the interaction between climate, vector abundance, and dengue incidence. Thoroughly understanding this correlation will allow the development of a customized and precise local early warning system. To achieve this purpose, we conducted a cohort study, during January-December 2017, in 16 districts in Bandung, West Java, Indonesia. In the selected areas, local weather stations and modified light mosquito traps were set up to obtain data regarding daily weather and the abundance of adult female Ae. aegypti. A generalized linear model was applied to analyze the effect of local weather and female adult Ae. aegypti on the number of dengue cases. The result showed a significant non-linear correlation among mosquito abundance, maximum temperature, and dengue cases. Using our model, the data showed that the addition of a single adult Ae. aegypti mosquito increased the risk of dengue infection by 1.8%, while increasing the maximum temperature by one degree decreased the risk by 17%. This finding suggests specific actionable insights needed to supplement existing mosquito eradication programs.

Predicting Infectious Diseases: A Bibliometric Review on Africa

Africa has a long history of novel and re-emerging infectious disease outbreaks. This reality has attracted the attention of researchers interested in the general research theme of predicting infectious diseases. However, a knowledge mapping analysis of literature to reveal the research trends, gaps, and hotspots in predicting Africa’s infectious diseases using bibliometric tools has not been conducted. A bibliometric analysis of 247 published papers on predicting infectious diseases in Africa, published in the Web of Science core collection databases, is presented in this study. The results indicate that the severe outbreaks of infectious diseases in Africa have increased scientific publications during the past decade. The results also reveal that African researchers are highly underrepresented in these publications and that the United States of America (USA) is the most productive and collaborative country. The relevant hotspots in this research field include malaria, models, classification, associations, COVID-19, and cost-effectiveness. Furthermore, weather-based prediction using meteorological factors is an emerging theme, and very few studies have used the fourth industrial revolution (4IR) technologies. Therefore, there is a need to explore 4IR predicting tools such as machine learning and consider integrated approaches that are pivotal to developing robust prediction systems for infectious diseases, especially in Africa. This review paper provides a useful resource for researchers, practitioners, and research funding agencies interested in the research theme—the prediction of infectious diseases in Africa—by capturing the current research hotspots and trends.

Aggregating human judgment probabilistic predictions of the safety, efficacy, and timing of a COVID-19 vaccine

Safe, efficacious vaccines were developed to reduce the transmission of SARS-CoV-2 during the COVID-19 pandemic. But in the middle of 2020, vaccine effectiveness, safety, and the timeline for when a vaccine would be approved and distributed to the public was uncertain. To support public health decision making, we solicited trained forecasters and experts in vaccinology and infectious disease to provide monthly probabilistic predictions from July to September of 2020 of the efficacy, safety, timing, and delivery of a COVID-19 vaccine. We found, that despite sparse historical data, a linear pool—a combination of human judgment probabilistic predictions—can quantify the uncertainty in clinical significance and timing of a potential vaccine. The linear pool underestimated how fast a therapy would show a survival benefit and the high efficacy of approved COVID-19 vaccines. However, the linear pool did make an accurate prediction for when a vaccine would be approved by the FDA. Compared to individual forecasters, the linear pool was consistently above the median of the most accurate forecasts. A linear pool is a fast and versatile method to build probabilistic predictions of a developing vaccine that is robust to poor individual predictions. Though experts and trained forecasters did underestimate the speed of development and the high efficacy of a SARS-CoV-2 vaccine, linear pool predictions can improve situational awareness for public health officials and for the public make clearer the risks, rewards, and timing of a vaccine.

Epidemic Risk Assessment by a Novel Communication Station Based Method

The COVID-19 pandemic has caused serious consequences in the last few months and trying to control it has been the most important objective. With effective prevention and control methods, the epidemic has been gradually under control in some countries and it is essential to ensure safe work resumption in the future. Although some approaches are proposed to measure people's healthy conditions, such as filling health information forms or evaluating people's travel records, they cannot provide a fine-grained assessment of the epidemic risk. In this paper, we propose a novel epidemic risk assessment method based on the granular data collected by the communication stations. We first compute the epidemic risk of these stations in different intervals by combining the number of infected persons and the way they pass through the station. Then, we calculate the personnel risk in different intervals according to the station trajectory of the queried person. This method could assess people's epidemic risk accurately and efficiently. We also conduct extensive simulations and the results verify the effectiveness of the proposed method.

Bridging implementation gaps to connect large ecological datasets and complex models

Merging robust statistical methods with complex simulation models is a frontier for improving ecological inference and forecasting. However, bringing these tools together is not always straightforward. Matching data with model output, determining starting conditions, and addressing high dimensionality are some of the complexities that arise when attempting to incorporate ecological field data with mechanistic models directly using sophisticated statistical methods. To illustrate these complexities and pragmatic paths forward, we present an analysis using tree-ring basal area reconstructions in Denali National Park (DNPP) to constrain successional trajectories of two spruce species (Picea mariana and Picea glauca) simulated by a forest gap model, University of Virginia Forest Model Enhanced-UVAFME. Through this process, we provide preliminary ecological inference about the long-term competitive dynamics between slow-growing P. mariana and relatively faster-growing P. glauca. Incorporating tree-ring data into UVAFME allowed us to estimate a bias correction for stand age with improved parameter estimates. We found that higher parameter values for P. mariana minimum growth under stress and P. glauca maximum growth rate were key to improving simulations of coexistence, agreeing with recent research that faster-growing P. glauca may outcompete P. mariana under climate change scenarios. The implementation challenges we highlight are a crucial part of the conversation for how to bring models together with data to improve ecological inference and forecasting.

Description of malaria epidemics and normal transmissions using rainfall variability in Gondar Zuria highland District, Ethiopia

Background

Rainfall is one of the climate variables most studied as it affects malaria occurrence directly.

Objective

This study aimed to describe how monthly rainfall variability affects malaria incidence in different years.

Methods

A total of 7 years (2013/14-2019/20) retrospective confirmed and treated malaria cases in Gondar Zuria district were used for analysis in addition to five (2013/14-2017/18) years retrospective data from Dembia district.

Results

The annual rainfalls in the study years showed no statistically significant difference (p = 0. 78). But, variations in rainfalls of the different months (p = 0.000) of the different years were the source of variations for malaria count (incidences) in the different years. Malaria was transmitted throughout the year with the highest peak in November (mean count = 1468.7 ± 697.8) and followed by May (mean count = 1253.4 ± 1391.8), after main Kiremt/Summer and minor Bulg/Spring rains respectively. The lowest transmission was occurred in February (338 ± 240.3) when the rivers were the only source of mosquito vectors. Year 2013/14 (RF = 2351.12 mm) and 2019/20 (RF = 2278.80 mm) with no statistically significant difference (p = 0.977) in annual rainfalls produced 10, 702 (49.2%) and 961 (20%) malaria counts for the Bulg (spring) season respectively due to 581.92 mm (24.8%) higher total Bulg/Spring rain in 2013/14 compared to 124.1 mm (5.45%) in 2019/20. Generally, above normal rainfalls in Bulg/Spring season increased malaria transmission by providing more aquatic habitats supporting the growth of the immature stages. But heavy rains in Summer/Kiremt produced low malaria counts due to the high intensity of the rainfalls which could kill the larvae and pupae. Spearman's correlation analysis indicated that the mean rainfalls of current month (RF) (0 lagged month) (P = 0.025), previous month (RF1) (1 month lagged) (p = 0.000), before previous months (RF2) (2 months lagged) (p = 0.001) and mean RF + RF1 + RF2 (P = 0.001) were positive significantly correlated with mean monthly malaria counts compared to negative significant correlations for temperature variables. Temperature variables negative correlations were interpreted as confounding effects because decreased malaria counts in dry months were due to a decrease in rainfalls. Conclusion: rainfall distribution in different months of a year affects malaria occurrences.

A Bayesian Spatio-Temporal Analysis of Malaria in the Greater Accra Region of Ghana from 2015 to 2019

The Greater Accra Region is the smallest of the 16 administrative regions in Ghana. It is highly populated and characterized by tropical climatic conditions. Although efforts towards malaria control in Ghana have had positive impacts, malaria remains in the top five diseases reported at healthcare facilities within the Greater Accra Region. To further accelerate progress, analysis of regionally generated data is needed to inform control and management measures at this level. This study aimed to examine the climatic drivers of malaria transmission in the Greater Accra Region and identify inter-district variation in malaria burden. Monthly malaria cases for the Greater Accra Region were obtained from the Ghanaian District Health Information and Management System. Malaria cases were decomposed using seasonal-trend decomposition, based on locally weighted regression to analyze seasonality. A negative binomial regression model with a conditional autoregressive prior structure was used to quantify associations between climatic variables and malaria risk and spatial dependence. Posterior parameters were estimated using Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. A total of 1,105,370 malaria cases were recorded in the region from 2015 to 2019. The overall malaria incidence for the region was approximately 47 per 1000 population. Malaria transmission was highly seasonal with an irregular inter-annual pattern. Monthly malaria case incidence was found to decrease by 2.3% (95% credible interval: 0.7–4.2%) for each 1 °C increase in monthly minimum temperature. Only five districts located in the south-central part of the region had a malaria incidence rate lower than the regional average at >95% probability level. The distribution of malaria cases was heterogeneous, seasonal, and significantly associated with climatic variables. Targeted malaria control and prevention in high-risk districts at the appropriate time points could result in a significant reduction in malaria transmission in the Greater Accra Region.

Urban Resilience of Shenzhen City under Climate Change

The Chinese government attaches great importance to climate change adaptation and has issued relevant strategies and policies. Overall, China’s action to adapt to climate change remains in its infancy, and relevant research needs to be further deepened. In this paper, we study the future adaptive countermeasures of Shenzhen city in the Pearl River Delta in terms of climate change, especially urban flood risk resilience. Based on the background investigation of urban flood risk in Shenzhen, this paper calculates the annual precipitation frequency of Shenzhen from 1953 to 2020, and uses the extreme precipitation index as a quantitative indicator to analyze the changes in historical precipitation and the impact of major flood disasters in Shenzhen city in previous decades. Based on the six kinds of model data of the scenario Model Inter-comparison Project (MIP) in the sixth phase of the Coupled Model Inter-comparison Project (CMIP6), uses the Taylor diagram and MR comprehensive evaluation method to evaluate the ability of different climate models to simulate extreme precipitation in Shenzhen, and the selected models are aggregated and averaged to predict the climate change trend of Shenzhen from 2020 to 2100. The prediction results show that Shenzhen will face more severe threats from rainstorms and floods in the future. Therefore, this paper proposes a resilience strategy for the city to cope with the threat of flood in the future, including constructing a smart water management system and promoting the development of a sponge city. Moreover, to a certain extent, it is necessary to realize risk transfer by promoting a flood insurance system.

Climate services for health: From global observations to local interventions

Despite the wealth of available climate data available, there is no consensus on the most appropriate product choice for health impact modelling and how this influences downstream climate-health decisions. We discuss challenges related to product choice, highlighting the importance of considering data biases and co-development of climate services between different sectors.

The influence of vector-borne disease on human history: socio-ecological mechanisms

Vector-borne diseases (VBDs) are embedded within complex socio-ecological systems. While research has traditionally focused on the direct effects of VBDs on human morbidity and mortality, it is increasingly clear that their impacts are much more pervasive. VBDs are dynamically linked to feedbacks between environmental conditions, vector ecology, disease burden, and societal responses that drive transmission. As a result, VBDs have had profound influence on human history. Mechanisms include: (1) killing or debilitating large numbers of people, with demographic and population-level impacts; (2) differentially affecting populations based on prior history of disease exposure, immunity, and resistance; (3) being weaponised to promote or justify hierarchies of power, colonialism, racism, classism and sexism; (4) catalysing changes in ideas, institutions, infrastructure, technologies and social practices in efforts to control disease outbreaks; and (5) changing human relationships with the land and environment. We use historical and archaeological evidence interpreted through an ecological lens to illustrate how VBDs have shaped society and culture, focusing on case studies from four pertinent VBDs: plague, malaria, yellow fever and trypanosomiasis. By comparing across diseases, time periods and geographies, we highlight the enormous scope and variety of mechanisms by which VBDs have influenced human history.

Achieving global malaria eradication in changing landscapes

Land use and land cover changes, such as deforestation, agricultural expansion and urbanization, are one of the largest anthropogenic environmental changes globally. Recent initiatives to evaluate the feasibility of malaria eradication have highlighted impacts of landscape changes on malaria transmission and the potential of these changes to undermine malaria control and elimination efforts. Multisectoral approaches are needed to detect and minimize negative impacts of land use and land cover changes on malaria transmission while supporting development aiding malaria control, elimination and ultimately eradication. Pathways through which land use and land cover changes disrupt social and ecological systems to increase or decrease malaria risks are outlined, identifying priorities and opportunities for a global malaria eradication campaign. The impacts of land use and land cover changes on malaria transmission are complex and highly context-specific, with effects changing over time and space. Landscape changes are only one element of a complex development process with wider economic and social dimensions affecting human health and wellbeing. While deforestation and other landscape changes threaten to undermine malaria control efforts and have driven the emergence of zoonotic malaria, most of the malaria elimination successes have been underpinned by agricultural development and land management. Malaria eradication is not feasible without addressing these changing risks while, conversely, consideration of malaria impacts in land management decisions has the potential to significantly accelerate progress towards eradication. Multisectoral cooperation and approaches to linking malaria control and environmental science, such as conducting locally relevant ecological monitoring, integrating landscape data into malaria surveillance systems and designing environmental management strategies to reduce malaria burdens, are essential to achieve malaria eradication.

Impact of environmental changes on infectious diseases: Key findings from an international conference in Trieste, Italy in May 2017

Elsevier's 2nd conference on "Impact of Environmental Changes on Infectious Diseases" (IECID), convened in May 2017 in Trieste, Italy, brought together some 120 researchers from more than 20 countries. They presented the latest findings and discussed the impact of current and predicted future environmental changes on infectious disease dynamics in humans, livestock and wildlife in different parts of the world. Particular emphasis was placed on food-, vector- and water-borne diseases within the general theme of infectious diseases of poverty and emerging and re-emerging diseases. The potential impact of mobility, travel, population growth, trade and globalization on infectious disease dynamics against the background of a changing climate, land use, air quality and urbanization on individual, population, ecosystem and planetary health were addressed. Speakers at the conference were encouraged to put forth their talks into stand-alone manuscripts, which resulted in a unique collection of 13 articles, now brought together into a thematic issue of Acta Tropica. In this umbrella piece, we synthesize key findings from the published articles and highlight potential actions that might be taken forward to prevent and mitigate the impact of environmental change on infectious diseases. The work presented is salient in the current era of the Sustainable Development Goals.

Malaria Exposure in Ann Township, Myanmar, as a Function of Land Cover and Land Use: Combining Satellite Earth Observations and Field Surveys

Despite progress toward malaria elimination in the Greater Mekong Subregion, challenges remain owing to the emergence of drug resistance and the persistence of focal transmission reservoirs. Malaria transmission foci in Myanmar are heterogeneous and complex, and many remaining infections are clinically silent, rendering them invisible to routine monitoring. The goal of this research is to define criteria for easy-to-implement methodologies, not reliant on routine monitoring, that can increase the efficiency of targeted malaria elimination strategies. Studies have shown relationships between malaria risk and land cover and land use (LCLU), which can be mapped using remote sensing methodologies. Here we aim to explain malaria risk as a function of LCLU for five rural villages in Myanmar's Rakhine State. Malaria prevalence and incidence data were analyzed through logistic regression with a land use survey of ~1,000 participants and a 30-m land cover map. Malaria prevalence per village ranged from 5% to 20% with the overwhelming majority of cases being subclinical. Villages with high forest cover were associated with increased risk of malaria, even for villagers who did not report visits to forests. Villagers living near croplands experienced decreased malaria risk unless they were directly engaged in farm work. Finally, land cover change (specifically, natural forest loss) appeared to be a substantial contributor to malaria risk in the region, although this was not confirmed through sensitivity analyses. Overall, this study demonstrates that remotely sensed data contextualized with field survey data can be used to inform critical targeting strategies in support of malaria elimination.

Deciphering Coral Disease Dynamics: Integrating Host, Microbiome, and the Changing Environment

Diseases of tropical reef organisms is an intensive area of study, but despite significant advances in methodology and the global knowledge base, identifying the proximate causes of disease outbreaks remains difficult. The dynamics of infectious wildlife diseases are known to be influenced by shifting interactions among the host, pathogen, and other members of the microbiome, and a collective body of work clearly demonstrates that this is also the case for the main foundation species on reefs, corals. Yet, among wildlife, outbreaks of coral diseases stand out as being driven largely by a changing environment. These outbreaks contributed not only to significant losses of coral species but also to whole ecosystem regime shifts. Here we suggest that to better decipher the disease dynamics of corals, we must integrate more holistic and modern paradigms that consider multiple and variable interactions among the three major players in epizootics: the host, its associated microbiome, and the environment. In this perspective, we discuss how expanding the pathogen component of the classic host-pathogen-environment disease triad to incorporate shifts in the microbiome leading to dysbiosis provides a better model for understanding coral disease dynamics. We outline and discuss issues arising when evaluating each component of this trio and make suggestions for bridging gaps between them. We further suggest that to best tackle these challenges, researchers must adjust standard paradigms, like the classic one pathogen-one disease model, that, to date, have been ineffectual at uncovering many of the emergent properties of coral reef disease dynamics. Lastly, we make recommendations for ways forward in the fields of marine disease ecology and the future of coral reef conservation and restoration given these observations.

Building resilience to mosquito-borne diseases in the Caribbean

Small island developing states in the Caribbean are among the most vulnerable countries on the planet to climate variability and climate change. In the last 3 decades, the Caribbean region has undergone frequent and intense heat waves, storms, floods, and droughts. This has had a detrimental impact on population health and well-being, including an increase in infectious disease outbreaks. Recent advances in climate science have enhanced our ability to anticipate hydrometeorological hazards and associated public health challenges. Here, we discuss progress towards bridging the gap between climate science and public health decision-making in the Caribbean to build health system resilience to extreme climatic events. We focus on the development of climate services to help manage mosquito-transmitted disease epidemics. There are numerous areas of ongoing biological research aimed at better understanding the direct and indirect impacts of climate change on the transmission of mosquito-borne diseases. Here, we emphasise additional factors that affect our ability to operationalise this biological understanding. We highlight a lack of financial resources, technical expertise, data sharing, and formalised partnerships between climate and health communities as major limiting factors to developing sustainable climate services for health. Recommendations include investing in integrated climate, health and mosquito surveillance systems, building regional and local human resource capacities, and designing national and regional cross-sectoral policies and national action plans. This will contribute towards achieving the Sustainable Development Goals (SDGs) and maximising regional development partnerships and co-benefits for improved health and well-being in the Caribbean.

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

Ecological forecasts to inform near-term management of threats to biodiversity

Ecosystems are being altered by rapid and interacting changes in natural processes and anthropogenic threats to biodiversity. Uncertainty in historical, current and future effectiveness of actions hampers decisions about how to mitigate changes to prevent biodiversity loss and species extinctions. Research in resource management, agriculture and health indicates that forecasts predicting the effects of near-term or seasonal environmental conditions on management greatly improve outcomes. Such forecasts help resolve uncertainties about when and how to operationalize management. We reviewed the scientific literature on environmental management to investigate whether near-term forecasts are developed to inform biodiversity decisions in Australia, a nation with one of the highest recent extinction rates across the globe. We found that forecasts focused on economic objectives (e.g. fisheries management) predict on significantly shorter timelines and answer a broader range of management questions than forecasts focused on biodiversity conservation. We then evaluated scientific literature on the effectiveness of 484 actions to manage seven major terrestrial threats in Australia, to identify opportunities for near-term forecasts to inform operational conservation decisions. Depending on the action, between 30% and 80% threat management operations experienced near-term weather impacts on outcomes before, during or after management. Disease control, species translocation/reintroduction and habitat restoration actions were most frequently impacted, and negative impacts such as increased species mortality and reduced recruitment were more likely than positive impacts. Drought or dry conditions, and rainfall, were the most frequently reported weather impacts, indicating that near-term forecasts predicting the effects of low or excessive rainfall on management outcomes are likely to have the greatest benefits. Across the world, many regions are, like Australia, becoming warmer and drier, or experiencing more extreme rainfall events. Informing conservation decisions with near-term and seasonal ecological forecasting will be critical to harness uncertainties and lower the risk of threat management failure under global change.

Early warning climate indices for malaria and meningitis in tropical ecological zones

This study aims at assessing the impacts of climate indices on the spatiotemporal distribution of malaria and meningitis in Nigeria. The primary focus of the research is to develop an Early Warning System (EWS) for assessing climate variability implications on malaria and meningitis spread in the study area. Both climate and health data were used in the study to determine the relationship between climate variability and the occurrence of malaria and meningitis. The assessment was based on variations in different ecological zones in Nigeria. Two specific sample locations were randomly selected in each ecological zone for the analysis. The climatic data used in this study are dekadal precipitation, minimum and maximum temperature between 2000 and 2018, monthly aerosol optical depth between 2000 and 2018. The results show that temperature is relatively high throughout the year because the country is located in a tropical region. The significant findings of this study are that rainfall has much influence on the occurrence of malaria, while temperature and aerosol have more impact on meningitis. We found the degree of relationship between precipitation and malaria, there is a correlation coefficient R2 ≥ 70.0 in Rainforest, Freshwater, and Mangrove ecological zones. The relationship between temperature and meningitis is accompanied by R2 ≥ 72.0 in both Sahel and Sudan, while aerosol and meningitis harbour R2 = 77.33 in the Sahel. The assessment of this initial data seems to support the finding that the occurrences of meningitis are higher in the northern region, especially the Sahel and Sudan. In contrast, malaria occurrence is higher in the southern part of the study area. In all, the multiple linear regression results revealed that rainfall was directly associated with malaria with β = 0.64, p = 0.001 but aerosol was directly associated with meningitis with β = 0.59, p < 0.001. The study concludes that variability in climatic elements such as low precipitation, high temperature, and aerosol may be the major drivers of meningitis occurrence.

Climate change and infectious disease research in Nepal: Are the available prerequisites supportive enough to researchers?

Although Nepal has been identified as a country highly vulnerable to adverse health and socioeconomic impacts arising from climate change, extant research on climate sensitive infectious diseases has yet to develop the evidence base to adequately address these threats. In this opinion paper we identify and characterise basic requirements that are hindering the progress of climate change and infectious disease research in Nepal. Our opinion is that immediate attention should be given to strengthening Nepal's public health surveillance system, promoting inter-sectoral collaboration, improving public health capacity, and enhancing community engagement in disease surveillance. Moreover, we advocate for greater technical support of public health researchers, and data sharing among data custodians and epidemiologists/researchers, to generate salient evidence to guide relevant public health policy formulation aimed at addressing the impacts of climate change on human health in Nepal. International studies on climate variability and infectious diseases have clearly demonstrated that climate sensitive diseases, namely vector-borne and food/water-borne diseases, are sensitive to climate variation and climate change. This research has driven the development and implementation of climate-based early warning systems for preventing potential outbreaks of climate-sensitive infectious diseases across many European and African countries. Similarly, we postulate that Nepal would greatly benefit from a climate-based early warning system, which would assist in identification or prediction of conditions suitable for disease emergence and facilitate a timely response to reduce mortality and morbidity during epidemics.

Quantifying Seasonal Variation in Insecticide-Treated Net Use among Those with Access

Seasonal variation in the proportion of the population using an insecticide-treated net (ITN) is well documented and is widely believed to be dependent on mosquito abundance and heat, driven by rainfall and temperature. However, seasonal variation in ITN use has not been quantified controlling for ITN access. Demographic and Health Survey and Malaria Indicator Survey datasets, their georeferenced data, and public rainfall and climate layers were pooled for 21 countries. Nine rainfall typologies were developed from rainfall patterns in Köppen climate zones. For each typology, the odds of ITN use among individuals with access to an ITN within their households (“ITN use given access”) were estimated for each month of the year, controlling for region, wealth quintile, residence, year, temperature, and malaria parasitemia level. Seasonality of ITN use given access was observed over all nine rainfall typologies and was most pronounced in arid climates and less pronounced where rainfall was relatively constant throughout the year. Peak ITN use occurred 1–3 months after peak rainfall and corresponded with peak malaria incidence and average malaria transmission season. The observed lags between peak rainfall and peak ITN use given access suggest that net use is triggered by mosquito density. In equatorial areas, ITN use is likely to be high year-round, given the presence of mosquitoes and an associated year-round perceived malaria risk. These results can be used to inform behavior change interventions to improve ITN use in specific times of the year and to inform geospatial models of the impact of ITNs on transmission.

Childhood malaria case incidence in Malawi between 2004 and 2017: spatio-temporal modelling of climate and non-climate factors

BACKGROUND

Malaria transmission is influenced by a complex interplay of factors including climate, socio-economic, environmental factors and interventions. Malaria control efforts across Africa have shown a mixed impact. Climate driven factors may play an increasing role with climate change. Efforts to strengthen routine facility-based monthly malaria data collection across Africa create an increasingly valuable data source to interpret burden trends and monitor control programme progress. A better understanding of the association with other climatic and non-climatic drivers of malaria incidence over time and space may help guide and interpret the impact of interventions.

METHODS

Routine monthly paediatric outpatient clinical malaria case data were compiled from 27 districts in Malawi between 2004 and 2017, and analysed in combination with data on climatic, environmental, socio-economic and interventional factors and district level population estimates. A spatio-temporal generalized linear mixed model was fitted using Bayesian inference, in order to quantify the strength of association of the various risk factors with district-level variation in clinical malaria rates in Malawi, and visualized using maps.

RESULTS

Between 2004 and 2017 reported childhood clinical malaria case rates showed a slight increase, from 50 to 53 cases per 1000 population, with considerable variation across the country between climatic zones. Climatic and environmental factors, including average monthly air temperature and rainfall anomalies, normalized difference vegetative index (NDVI) and RDT use for diagnosis showed a significant relationship with malaria incidence. Temperature in the current month and in each of the 3 months prior showed a significant relationship with the disease incidence unlike rainfall anomaly which was associated with malaria incidence at only three months prior. Estimated risk maps show relatively high risk along the lake and Shire valley regions of Malawi.

CONCLUSION

The modelling approach can identify locations likely to have unusually high or low risk of malaria incidence across Malawi, and distinguishes between contributions to risk that can be explained by measured risk-factors and unexplained residual spatial variation. Also, spatial statistical methods applied to readily available routine data provides an alternative information source that can supplement survey data in policy development and implementation to direct surveillance and intervention efforts.

What is Machine Learning? A Primer for the Epidemiologist

Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on "Big Data," it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.

El Niño-Southern oscillation and under-5 diarrhea in Botswana

Childhood diarrheal disease causes significant morbidity and mortality in low and middle-income countries, yet our ability to accurately predict diarrhea incidence remains limited. El Niño-Southern Oscillation (ENSO) has been shown to affect diarrhea dynamics in South America and Asia. However, understanding of its effects in sub-Saharan Africa, where the burden of under-5 diarrhea is high, remains inadequate. Here we investigate the connections between ENSO, local environmental conditions, and childhood diarrheal disease in Chobe District, Botswana. Our results demonstrate that La Niña conditions are associated with cooler temperatures, increased rainfall, and higher flooding in the Chobe region during the rainy season. In turn, La Niña conditions lagged 0–5 months are associated with higher than average incidence of under-5 diarrhea in the early rainy season. These findings demonstrate the potential use of ENSO as a long-lead prediction tool for childhood diarrhea in southern Africa.

Here, Heaney et al. show that La Niña conditions are associated with higher than average incidence of childhood diarrheal disease in Botswana in the early rainy season. This finding could help to predict childhood diarrhea outbreaks in southern Africa.

Malaria predictions based on seasonal climate forecasts in South Africa: A time series distributed lag nonlinear model

Although there have been enormous demands and efforts to develop an early warning system for malaria, no sustainable system has remained. Well-organized malaria surveillance and high-quality climate forecasts are required to sustain a malaria early warning system in conjunction with an effective malaria prediction model. We aimed to develop a weather-based malaria prediction model using a weekly time-series data including temperature, precipitation, and malaria cases from 1998 to 2015 in Vhembe, Limpopo, South Africa and apply it to seasonal climate forecasts. The malaria prediction model performed well for short-term predictions (correlation coefficient, r > 0.8 for 1- and 2-week ahead forecasts). The prediction accuracy decreased as the lead time increased but retained fairly good performance (r > 0.7) up to the 16-week ahead prediction. The demonstration of the malaria prediction process based on the seasonal climate forecasts showed the short-term predictions coincided closely with the observed malaria cases. The weather-based malaria prediction model we developed could be applicable in practice together with skillful seasonal climate forecasts and existing malaria surveillance data. Establishing an automated operating system based on real-time data inputs will be beneficial for the malaria early warning system, and can be an instructive example for other malaria-endemic areas.

Evaluation and Projection of Extreme Precipitation over Northern China in CMIP5 Models

This study evaluates 32 climate models from CMIP5 compared with a daily gridded observation dataset of extreme precipitation indices including total extreme precipitation (R95p), maximum consecutive five days of precipitation (RX5day) and wet days larger than 10 mm of precipitation (R10mm) over Northern China during the historical period (1986–2005). Results show the majority models have good performance on spatial distribution but overestimate the amplitude of precipitation over Northern China. Most models can also capture interannual variation of R95p and RX5d, but with poor simulations on R10mm. Considering both spatial and temporal factors, the best multi-model ensemble (Group 1) has been selected and improved by 42%, 34%, and 37% for R95p, RX5d, and R10mm, respectively. Projection of extreme precipitation indicates that the fastest-rising region is in Northwest China due to the enhanced rainfall intensity. However, the uncertainty analysis shows the increase of extreme rainfall over Northwest China has a low confidence level. The projection of increasing extreme rainfall over Northeast China from Group 1 due to the longer extreme rainfall days is more credible. The weak subtropical high and southwest winds from Arabian Sea lead to the low wet biases from Group 1 and the cyclonic anomalies over Northeast China, which result in more extreme precipitation.

Thermal biology of mosquito-borne disease

Mosquito-borne diseases cause a major burden of disease worldwide. The vital rates of these ectothermic vectors and parasites respond strongly and nonlinearly to temperature and therefore to climate change. Here, we review how trait-based approaches can synthesise and mechanistically predict the temperature dependence of transmission across vectors, pathogens, and environments. We present 11 pathogens transmitted by 15 different mosquito species - including globally important diseases like malaria, dengue, and Zika - synthesised from previously published studies. Transmission varied strongly and unimodally with temperature, peaking at 23-29ºC and declining to zero below 9-23ºC and above 32-38ºC. Different traits restricted transmission at low versus high temperatures, and temperature effects on transmission varied by both mosquito and parasite species. Temperate pathogens exhibit broader thermal ranges and cooler thermal minima and optima than tropical pathogens. Among tropical pathogens, malaria and Ross River virus had lower thermal optima (25-26ºC) while dengue and Zika viruses had the highest (29ºC) thermal optima. We expect warming to increase transmission below thermal optima but decrease transmission above optima. Key directions for future work include linking mechanistic models to field transmission, combining temperature effects with control measures, incorporating trait variation and temperature variation, and investigating climate adaptation and migration.

Aggravation of Human Diseases and Climate Change Nexus

For decades, researchers have debated whether climate change has an adverse impact on diseases, especially infectious diseases. They have identified a strong relationship between climate variables and vector's growth, mortality rate, reproduction, and spatiotemporal distribution. Epidemiological data further indicates the emergence and re-emergence of infectious diseases post every single extreme weather event. Based on studies conducted mostly between 1990-2018, three aspects that resemble the impact of climate change impact on diseases are: (a) emergence and re-emergence of vector-borne diseases, (b) impact of extreme weather events, and (c) social upliftment with education and adaptation. This review mainly examines and discusses the impact of climate change based on scientific evidences in published literature. Humans are highly vulnerable to diseases and other post-catastrophic effects of extreme events, as evidenced in literature. It is high time that human beings understand the adverse impacts of climate change and take proper and sustainable control measures. There is also the important requirement for allocation of effective technologies, maintenance of healthy lifestyles, and public education.

The influence of weather and weather variability on mosquito abundance and infection with West Nile virus in Harris County, Texas, USA

Early warning systems for vector-borne diseases (VBDs) prediction are an ecological application where data from the interface of several environmental components can be used to predict future VBD transmission. In general, models for early warning systems only consider average environmental conditions ignoring variation in weather variables, despite the prediction from Schmalhausen's law about the importance of environmental variability for biological systems. We present results from a long-term mosquito surveillance program from Harris County, Texas, USA, where we use time series analysis techniques to study the abundance and West Nile virus (WNV) infection patterns in the local primary vector, Culex quinquefasciatus Say. We found that, as predicted by Schmalhausen's law, mosquito abundance was associated with the standard deviation and kurtosis of environmental variables. By contrast, WNV infection rates were associated with 8-month lagged temperature, suggesting environmental conditions during overwintering might be key for WNV amplification during summer outbreaks. Finally, model validation showed that seasonal autoregressive models successfully predicted mosquito WNV infection rates up to 2 months ahead, but did rather poorly at predicting mosquito abundance, a result that might reflect impacts of vector control for mosquito population reduction, geographic scale, and other artifacts generated by operational constraints of mosquito surveillance systems.

Weather-based forecasting of mosquito-borne disease outbreaks in Canada

Early warning systems to predict infectious disease outbreaks have been identified as a key adaptive response to climate change. Warming, climate variability and extreme weather events associated with climate change are expected to drive an increase in frequency and intensity of mosquito-borne disease (MBD) outbreaks globally. In Canada, this will mean an increased risk of endemic and emerging MBD outbreaks such as West Nile virus and other MBDs. The availability of timely information on the risk of impending MBD outbreaks has important public health implications, by allowing implementation of mosquito control measures and targeted communications regarding the need for increased personal protective measures-before an outbreak occurs. In Canada, both mechanistic and statistical weather-based models have been developed to predict West Nile virus outbreaks. These include models for different species of mosquitoes that transmit West Nile virus in different geographical areas of Canada. Although initial results have been promising, further validation and assessment of forecasting skill are needed before wide scale implementation. Weather-based forecasting for other emerging MBDs in Canada, such as Eastern equine encephalitis, may also be feasible.

The Future of Climate Epidemiology: Opportunities for Advancing Health Research in the Context of Climate Change

In the coming decades, climate change is expected to dramatically affect communities worldwide, altering the patterns of many ambient exposures and disasters, including extreme temperatures, heat waves, wildfires, droughts, and floods. These exposures, in turn, can affect risks for a variety of human diseases and health outcomes. Climate epidemiology plays an important role in informing policy related to climate change and its threats to public health. Climate epidemiology leverages deep, integrated collaborations between epidemiologists and climate scientists to understand the current and potential future impacts of climate-related exposures on human health. A variety of recent and ongoing developments in climate science are creating new avenues for epidemiologic contributions. Here, we discuss the contributions of climate epidemiology and describe some key current research directions, including research to better characterize uncertainty in climate health projections. We end by outlining 3 developing areas of climate science that are creating opportunities for high-impact epidemiologic advances in the near future: 1) climate attribution studies, 2) subseasonal to seasonal forecasts, and 3) decadal predictions.

Impact of Weekly Climatic Variables on Weekly Malaria Incidence throughout Thailand: A Country-Based Six-Year Retrospective Study

Purpose. This study aimed to evaluate climatic data, including mean temperature, relative humidity, and rainfall, and their association with malaria incidence throughout Thailand from 2012 to 2017. The correlation of climatic parameters including temperature, relative humidity, and rainfall in each province and the weekly malaria incidence was analyzed using Spearman's rank correlation. The results showed that the mean temperature correlated with malaria incidence (p value < 0.05) in 44 provinces in Thailand. These correlations were frequently found in the western and southern parts of Thailand. Relative humidity correlated with malaria incidence (p value < 0.05) in 35 provinces. These correlations were frequently found in the northern and northeastern parts of Thailand. Rainfall correlated with malaria incidence (p value < 0.05) in 38 provinces. These correlations were frequently found in the northern parts and some western parts of Thailand. The impacts of the mean temperature, relative humidity, and rainfall were observed frequently in specific provinces, including Chiang Mai, Chiang Rai, Trat, Kanchanaburi, Ubonratchathani, and Si Sa Ket. This is the first study to report areas where climatic data are associated with malaria incidence throughout Thailand from 2012 to 2017. These results can map out the climatic change process over time and across the country, which is the foundation for effective early warning systems for malaria, public health awareness campaigns, and the adoption of proper adaption measures that will help in malaria detection, diagnosis, and treatment.

Decline in malaria incidence in a typical county of China: Role of climate variance and anti-malaria intervention measures

Malaria is an important vector-borne disease which is widespread in tropical and subtropical areas worldwide as well as in south China. Previous research has separately focused on the association between malaria incidence and meteorological variables or between malaria incidence and anti-malaria intervention measures in China, especially in Yunnan Province. Therefore, a typical county, Tengchong County, in Yunnan Province with high malaria incidence was selected as the study area to investigate the integrated influence of climate variance and anti-malaria intervention measures. Malaria incidence and meteorological variables were analyzed with a 2-month lag. The variables include average monthly temperature, minimum temperature, maximum temperature, cumulative precipitation, wind speed, maximum wind speed, relative humidity and minimum relative humidity. First, the principal component analysis was introduced to investigate the relationship between malaria incidence and meteorological variables; classification and regression trees were used to clarify contributions of key meteorological variables to malaria incidence afterwards. Second, based on existing anti-malaria intervention measures and above results, the integrated impact of climate variance and anti-malaria interventions on interannual trends of malaria incidence was analyzed. High malaria incidence occurred under one of the two meteorological conditions: 1) high minimum temperature combined with high minimum relative humidity or both precipitation and minimum relative humidity above middle level; 2) middle minimum temperature combined with both precipitation and minimum relative humidity below middle levels. Moreover, the steep interannual decline of malaria incidence in Tengchong was determined by slight climate variance and persistent anti-malaria intervention measures during malaria epidemics, predominantly by the latter. These findings will provide evidence data for developing malaria surveillance strategies in China.

Relationship between Flooding and Out Break of Infectious Diseasesin Kenya: A Review of the Literature

Flooding can potentially increase the spread of infectious diseases. To enhance good understanding of the health consequences of flooding and facilitate planning for mitigation strategies, deeper consideration of the relationship between flooding and out-break of infectious diseases is required. This paper examines the relationship between occurrence of floods in Kenya and outbreak of infectious diseases and possible interventions. This review intended to build up the quality and comprehensiveness of evidence on infectious diseases arising after flooding incidence in Kenya. An extensive literature review was conducted in 2017, and published literature from 2000 to 2017 was retrieved. This review suggests that infectious disease outbreaks such as waterborne, rodent-borne, and vector-borne diseases have been associated with flooding in Kenya. But there is need for more good quality epidemiological data to cement the evidence. Comprehensive surveillance and risk assessment, early warning systems, emergency planning, and well-coordinated collaborations are essential in reducing future vulnerability to infectious diseases following flooding.

Uncertainty in malaria simulations in the highlands of Kenya: Relative contributions of model parameter setting, driving climate and initial condition errors

In this study, experiments are conducted to gauge the relative importance of model, initial condition, and driving climate uncertainty for simulations of malaria transmission at a highland plantation in Kericho, Kenya. A genetic algorithm calibrates each of these three factors within their assessed prior uncertainty in turn to see which allows the best fit to a timeseries of confirmed cases. It is shown that for high altitude locations close to the threshold for transmission, the spatial representativeness uncertainty for climate, in particular temperature, dominates the uncertainty due to model parameter settings. Initial condition uncertainty plays little role after the first two years, and is thus important in the early warning system context, but negligible for decadal and climate change investigations. Thus, while reducing uncertainty in the model parameters would improve the quality of the simulations, the uncertainty in the temperature driving data is critical. It is emphasized that this result is a function of the mean climate of the location itself, and it is shown that model uncertainty would be relatively more important at warmer, lower altitude locations.

Climate drivers of vector-borne diseases in Africa and their relevance to control programmes

Background

Climate-based disease forecasting has been proposed as a potential tool in climate change adaptation for the health sector. Here we explore the relevance of climate data, drivers and predictions for vector-borne disease control efforts in Africa.

Methods

Using data from a number of sources we explore rainfall and temperature across the African continent, from seasonality to variability at annual, multi-decadal and timescales consistent with climate change. We give particular attention to three regions defined as WHO-TDR study zones in Western, Eastern and Southern Africa. Our analyses include 1) time scale decomposition to establish the relative importance of year-to-year, decadal and long term trends in rainfall and temperature; 2) the impact of the El Niño Southern Oscillation (ENSO) on rainfall and temperature at the Pan African scale; 3) the impact of ENSO on the climate of Tanzania using high resolution climate products and 4) the potential predictability of the climate in different regions and seasons using Generalized Relative Operating Characteristics. We use these analyses to review the relevance of climate forecasts for applications in vector borne disease control across the continent.

Results

Timescale decomposition revealed long term warming in all three regions of Africa – at the level of 0.1–0.3 °C per decade. Decadal variations in rainfall were apparent in all regions and particularly pronounced in the Sahel and during the East African long rains (March–May). Year-to-year variability in both rainfall and temperature, in part associated with ENSO, were the dominant signal for climate variations on any timescale. Observed climate data and seasonal climate forecasts were identified as the most relevant sources of climate information for use in early warning systems for vector-borne diseases but the latter varied in skill by region and season.

Conclusions

Adaptation to the vector-borne disease risks of climate variability and change is a priority for government and civil society in African countries. Understanding rainfall and temperature variations and trends at multiple timescales and their potential predictability is a necessary first step in the incorporation of relevant climate information into vector-borne disease control decision-making.

Electronic supplementary material

The online version of this article (10.1186/s40249-018-0460-1) contains supplementary material, which is available to authorized users.

El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers

The El Niño Southern Oscillation (ENSO) is an important mode of climatic variability that exerts a discernible impact on ecosystems and society through alterations in climate patterns. For this reason, ENSO has attracted much interest in the climate and health science community, with many analysts investigating ENSO health links through considering the degree of dependency of the incidence of a range of climate diseases on the occurrence of El Niño events. Because of the mounting interest in the relationship between ENSO as a major mode of climatic variability and health, this paper presents an overview of the basic characteristics of the ENSO phenomenon and its climate impacts, discusses the use of ENSO indices in climate and health research, and outlines the present understanding of ENSO health associations. Also touched upon are ENSO-based seasonal health forecasting and the possible impacts of climate change on ENSO and the implications this holds for future assessments of ENSO health associations. The review concludes that there is still some way to go before a thorough understanding of the association between ENSO and health is achieved, with a need to move beyond analyses undertaken through a purely statistical lens, with due acknowledgement that ENSO is a complex non-canonical phenomenon, and that simple ENSO health associations should not be expected.

Spatio-temporal modelling of weekly malaria incidence in children under 5 for early epidemic detection in Mozambique

Malaria is a major cause of morbidity and mortality in Mozambique. We present a malaria early warning system (MEWS) for Mozambique informed by seven years of weekly case reports of malaria in children under 5 years of age from 142 districts. A spatio-temporal model was developed based on explanatory climatic variables to map exceedance probabilities, defined as the predictive probability that the relative risk of malaria incidence in a given district for a particular week will exceed a predefined threshold. Unlike most spatially discrete models, our approach accounts for the geographical extent of each district in the derivation of the spatial covariance structure to allow for changes in administrative boundaries over time. The MEWS can thus be used to predict areas that may experience increases in malaria transmission beyond expected levels, early enough so that prevention and response measures can be implemented prior to the onset of outbreaks. The framework we present is also applicable to other climate-sensitive diseases.

Impact of climate variability on the transmission risk of malaria in northern Côte d'Ivoire

Since the 1970s, the northern part of Côte d'Ivoire has experienced considerable fluctuation in its meteorology including a general decrease of rainfall and increase of temperature from 1970 to 2000, a slight increase of rainfall since 2000, a severe drought in 2004-2005 and flooding in 2006-2007. Such changing climate patterns might affect the transmission of malaria. The purpose of this study was to analyze climate and environmental parameters associated with malaria transmission in Korhogo, a city in northern Côte d'Ivoire. All data were collected over a 10-year period (2004-2013). Rainfall, temperature and Normalized Difference Vegetation Index (NDVI) were the climate and environmental variables considered. Association between these variables and clinical malaria data was determined, using negative binomial regression models. From 2004 to 2013, there was an increase in the annual average precipitation (1100.3-1376.5 mm) and the average temperature (27.2°C-27.5°C). The NDVI decreased from 0.42 to 0.40. We observed a strong seasonality in these climatic variables, which resembled the seasonality in clinical malaria. An incremental increase of 10 mm of monthly precipitation was, on average, associated with a 1% (95% Confidence interval (CI): 0.7 to 1.2%) and a 1.2% (95% CI: 0.9 to 1.5%) increase in the number of clinical malaria episodes one and two months later respectively. A 1°C increase in average monthly temperature was, on average, associated with a decline of a 3.5% (95% CI: 0.1 to 6.7%) in clinical malaria episodes. A 0.1 unit increase in monthly NDVI was associated with a 7.3% (95% CI: 0.8 to 14.1%) increase in the monthly malaria count. There was a similar increase for the preceding-month lag (6.7% (95% CI: 2.3% to 11.2%)). The study results can be used to establish a malaria early warning system in Korhogo to prepare for outbreaks of malaria, which would increase community resilience no matter the magnitude and pattern of climate change.

Spores and soil from six sides: interdisciplinarity and the environmental biology of anthrax (Bacillus anthracis)

Environmentally transmitted diseases are comparatively poorly understood and managed, and their ecology is particularly understudied. Here we identify challenges of studying environmental transmission and persistence with a six-sided interdisciplinary review of the biology of anthrax (Bacillus anthracis). Anthrax is a zoonotic disease capable of maintaining infectious spore banks in soil for decades (or even potentially centuries), and the mechanisms of its environmental persistence have been the topic of significant research and controversy. Where anthrax is endemic, it plays an important ecological role, shaping the dynamics of entire herbivore communities. The complex eco-epidemiology of anthrax, and the mysterious biology of Bacillus anthracis during its environmental stage, have necessitated an interdisciplinary approach to pathogen research. Here, we illustrate different disciplinary perspectives through key advances made by researchers working in Etosha National Park, a long-term ecological research site in Namibia that has exemplified the complexities of the enzootic process of anthrax over decades of surveillance. In Etosha, the role of scavengers and alternative routes (waterborne transmission and flies) has proved unimportant relative to the long-term persistence of anthrax spores in soil and their infection of herbivore hosts. Carcass deposition facilitates green-ups of vegetation to attract herbivores, potentially facilitated by the role of anthrax spores in the rhizosphere. The underlying seasonal pattern of vegetation, and herbivores' immune and behavioural responses to anthrax risk, interact to produce regular 'anthrax seasons' that appear to be a stable feature of the Etosha ecosystem. Through the lens of microbiologists, geneticists, immunologists, ecologists, epidemiologists, and clinicians, we discuss how anthrax dynamics are shaped at the smallest scale by population genetics and interactions within the bacterial communities up to the broadest scales of ecosystem structure. We illustrate the benefits and challenges of this interdisciplinary approach to disease ecology, and suggest ways anthrax might offer insights into the biology of other important pathogens. Bacillus anthracis, and the more recently emerged Bacillus cereus biovar anthracis, share key features with other environmentally transmitted pathogens, including several zoonoses and panzootics of special interest for global health and conservation efforts. Understanding the dynamics of anthrax, and developing interdisciplinary research programs that explore environmental persistence, is a critical step forward for understanding these emerging threats.

Interactions between climatic changes and intervention effects on malaria spatio-temporal dynamics in Uganda

Background

Although malaria burden in Uganda has declined since 2009 following the scale-up of interventions, the disease is still the leading cause of hospitalization and death. Transmission remains high and is driven by suitable weather conditions. There is a real concern that intervention gains may be reversed by climatic changes in the country. In this study, we investigate the effects of climate on the spatio-temporal trends of malaria incidence in Uganda during 2013–2017.

Methods

Bayesian spatio-temporal negative binomial models were fitted on district-aggregated monthly malaria cases, reported by two age groups, defined by a cut-off age of 5 years. Weather data was obtained from remote sensing sources including rainfall, day land surface temperature (LSTD) and night land surface temperature (LSTN), Normalized Difference Vegetation Index (NDVI), altitude, land cover, and distance to water bodies. Spatial and temporal correlations were taken into account by assuming a conditional autoregressive and a first-order autoregressive process on district and monthly specific random effects, respectively. Fourier trigonometric functions modeled seasonal fluctuations in malaria transmission. The effects of climatic changes on the malaria incidence changes between 2013 and 2017 were estimated by modeling the difference in time varying climatic conditions at the two time points and adjusting for the effects of intervention coverage, socio-economic status and health seeking behavior.

Results

Malaria incidence declined steadily from 2013 to 2015 and then increased in 2016. The decrease was by over 38% and 20% in children <5 years and individuals ≥5 years, respectively. Temporal trends depict a strong bi-annual seasonal pattern with two peaks during April–June and October–December. The annual average of rainfall, LSTD and LSTN increased by 3.7 mm, 2.2 °C and 1.0 °C, respectively, between 2013 and 2017, whereas NDVI decreased by 6.8%. On the one hand, the increase in LSTD and decrease in NDVI were associated with a reduction in the incidence decline. On the other hand, malaria interventions and treatment seeking behavior had reverse effects, that were stronger compared to the effects of climatic changes. Important interactions between interventions with NDVI and LSTD suggest a varying impact of interventions on malaria burden in different climatic conditions.

Conclusion

Climatic changes in Uganda during the last five years contributed to a favorable environment for malaria transmission, and had a detrimental effect on malaria reduction gains achieved through interventions scale-up efforts. The NMCP should create synergies with the National Meteorological Authority with an ultimate goal of developing a Malaria Early Warning System to mitigate adverse climatic change effects on malaria risk in the country.

Mosquito-Borne Diseases: Advances in Modelling Climate-Change Impacts

Vector-borne diseases are on the rise globally. As the consequences of climate change are becoming evident, climate-based models of disease risk are of growing importance. Here, we review the current state-of-the-art in both mechanistic and correlative disease modelling, the data driving these models, the vectors and diseases covered, and climate models applied to assess future risk. We find that modelling techniques have advanced considerably, especially in terms of using ensembles of climate models and scenarios. Effects of extreme events, precipitation regimes, and seasonality on diseases are still poorly studied. Thorough validation of models is still a challenge and is complicated by a lack of field and laboratory data. On a larger scale, the main challenges today lie in cross-disciplinary and cross-sectoral transfer of data and methods.