Vector–virus-microclimate surveillance and research platform for dengue control in Machala, Ecuador.
Mercy J. Borbor-Cordovaa, Efraín Beltrán Ayalab,c, Washington B. Cardenasa, Timothy Endyd, Julia L. Finkelsteine, Christine A. Kingd, Renato Leoni, Ángel G. Muñozf, Raúl Mejíag, Mark E. Polhemusd, G. Cristina Recalde-Coronela, Sadie J. Ryand,h, Anna M. Stewart-Ibarrad
a Escuela Superior Politécnica del Litoral (ESPOL), Guayaquil, Ecuador
b Facultad de Medicina, Universidad Técnica de Machala (UTM), Machala, Ecuador
c The Ministry of Health (MSP), Machala, Ecuador.
d Center for Global Health and Translational Science, State University of New York Upstate Medical University (SUNY UMU), Syracuse, NY, USA
e Division of Nutritional Sciences, Cornell University, Ithaca, NY, USA.
f International Research Institute for Climate and Society (IRI), Earth Institute, Columbia University, New York, NY, USA.
g Instituto Nacional de Hidrología y Meteorología del Ecuador (INAMHI), Guayaquil, Ecuador
h Department of Geography, Emerging Pathogens Institute, University of Florida (UF), Gainesville, FL, USA
i Universidad San Francisco de Quito, Laboratorio de Entomología Médica, Quito, Ecuador.
Machala, a city located in southern coastal Ecuador, has been a strategic dengue research site since 2010, following one of the largest dengue epidemics on record. Every year dengue season in Machala represents a big resources investment from the public health sector. In this context, the National Institute of Meteorology and Hydrology (INAMHI), the Ministry of Health (MSP) of Ecuador, and an international research team have co-developed an integrated dengue-climate research and surveillance platform. The team has generated the evidence base for the effects of climate on dengue fever and strengthened the local research and surveillance capacities. This comprehensive surveillance system is generating fine-scale spatiotemporal data on microclimate, virus and vector dynamics, and sociodemographic risk factors, allowing investigators to determine the true burden of dengue illness and local climate and non-climate triggers. Micro-climate information is generated by five weather sensors and an automated full meteorological station operated by INAMHI. Currently we are developing an urban microclimate mapping together with a multivariate cluster analysis of the risk factors of dengue transmission across the city of Machala. Partial results are presented in this opportunity.
Weather and climate change impacts on human mortality in Bangladesh
Katrin Burkart, PhD1, Corey Lesk, MSc2, Daniel Bader, PhD2, Radley Horton, PhD2,
Patrick Kinney, ScD1
1 Columbia University, Mailman School of Public Health, New York, United States
2 Columbia University, Earth Institute, New York, United States
Weather and climate profoundly affect human health. Several studies have demonstrated a U-, V-, or J-shaped temperature-mortality relationship with increasing death rates at the lower and particularly upper end of the temperature distribution. The objectives of this study were (1) to analyze the relationship between temperature and mortality in Bangladesh for different subpopulations and (2) to project future heat-related mortality under climate change scenarios. We used (non-)parametric Generalized Additive Models adjusted for trend, season and day of the month to analyze the effect of temperature on daily mortality. We found a decrease in mortality with increasing temperature over a wide range of values; between the 90th and 95th percentile an abrupt increase in mortality was observed which was particularly pronounced for the elderly above the age of 65 years, for males, as well as in urban areas and in areas with a high socio-economic status. Daily historical and future temperature values were obtained from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset. This dataset is comprised of downscaled climate scenarios for the globe that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5). The derived dose-response functions were used to estimate the number of heat-related deaths occurring during the 1990s (1980-2005), the 2020s (2011-2040) and the 2050s (2041-2070). We estimated that excess deaths due to heat will triple from the 1990s to the 2050s, with an annual number of 0.5 million excess deaths in 1990 to and expected number of 1.5 millions in 2050.
Mapping Climatic and Non-Climatic Determinants of Malaria in Malawi for Designing Transmission Reduction Tools
James Chirombo, MSc1,2, Rachel Lowe, PhD3, Dianne J. Terlouw, PhD2, Jonathan M. Read, PhD1, Pietro Ceccato, PhD4, Madeleine C. Thomson, PhD4, Peter J. Diggle, PhD1
1CHICAS, Lancaster University Medical School, Lancaster, United Kingdom
2Malawi Liverpool Wellcome Trust Clinical Research Programme, Blantyre, Malawi
3Institut Català de Ciències del Clima (IC3), Barcelona, Spain
4International Research Institute for Climate and Society, New York, New York, United States
Malaria incidence reduction in malaria endemic areas of the world is now a key focus of national control programs. Transmission hotspots identification, through profiling of geographical variation in malaria over time, will play a key role in this endeavour especially in resource constrained settings like Malawi. Spatio-temporal statistical models and Bayesian predictive inference are well suited to mapping health outcomes in low resource settings. Their ability to capture spatial variation at large and small scales is key for designing control interventions. We use spatio-temporal statistical models to investigate the contribution of climatic, environmental and socio-economic factors to district-level variation in malaria risk in Malawi. Malaria data for the analysis are taken from an age stratified health management information system data covering all 28 districts in Malawi between July 2004 and December 2015 while socio-economic data are obtained from national surveys such as the Malawi demographic and health survey. We use remotely sensed climate and environmental data averaged over the districts to capture their impact on malaria transmission. We assessed these covariates in a non-spatial generalized linear model to identify significant drivers of malaria, which we then used to build the Bayesian hierarchical model and generate predictive risk maps to reveal spatial variation in disease risk. Through these maps, it is possible to identify areas of unusually high and low risk that could inform sub-district surveillance and control strategies. This climate based model approach could be a key component of an integrated surveillance system for use by malaria control programs
Optimized and Scalable Climate Data Services
John del Corral, BA, M. Benno Blumenthal, PhD, Michael Bell, MS, Remi Cousin, MS, Haibo Liu, PhD
International Research Institute for Climate and Society, Earth Institute, Columbia University, New York, New York, United States
IRI Climate Data Library (http://iridl.ldeo.columbia.edu) is designed to optimize the display, analysis, and retrieval of climate datasets. These datasets range from simple station observations, to multi-ensemble climate model results, to high-resolution satellite measurements, to GIS representations of geographic entities. These datasets are represented in a consistent multi-dimensional framework. As a result, station observations can easily be compared with climate model results, and satellite measurements. Gridded data can be spatially averaged over discrete geographic entities. The Climate Data Library is accessible with a browser connected to the Internet or local area network. The Climate Data Library servers perform the data selection, processing, and analysis. The resulting images or data files are sent back to the client’s desktop. This model optimizes the use of Internet bandwidth. The software required is open source and available in most parts of the world. The Data Library software can be installed on a personal computer, in a data center, or in multiple data centers connected together.
Enhancing National Climate Services to Support Climate-Resilient Development in Africa
Tufa Dinku, Remi Cousin, John Del Corral, Rija Faniriantsoa, Madeleine Thomson, Igor Khomyakove and Audrey Vadillo
International Research Institute for Climate and Society (IRI), The Earth Institute at Columbia University, Palisades, New York
Decision-relevant information on the past climate, recent trends, likely future trajectories, and associated impacts could play a critical role in national development planning; helping policy and decision makers to better manage climate risks and maximize opportunities. Climate information could be used to support early warning systems that alert decision-makers to potential food insecurity, or to better map populations and systems where climate poses a risk and thus improve long term planning.
However, it is disconcerting to note that climate information is not widely used in Africa to make development decisions. This is mainly because useful information is often not available or, if it does exist, is inaccessible to those that need it most.
The International Research Institute for Climate and Society (IRI), in collaboration with National Meteorological and Hydrological Services (NMHS) and other partners, has been leading an ambitious effort to simultaneously improve the availability, access and use of climate information at the national level. The Enhancing National Climate Services (ENACTS) initiative focuses on enabling NMHS to create reliable climate information that is suitable for national and local decision-making. Data availability is improved by blending national observations with satellite and other proxies. Data access and use is improved by providing online tools for data visualization and download and training users. The online tools are integrated into the NMHS web pages. The ENACTS approach overcomes traditional barriers in data quality and access. The spatially and temporally continuous datasets allow for characterization of climate risks at a local scale, and offer a low-cost, high impact opportunity with major potential to support climate resilient development.
CHICAS: Geospatial Health Informatics Capability
Lancaster Medical School, Lancaster University, UK
“Combining Health Information, Computation And Statistics” (CHICAS) is a research group of statisticians and epidemiologists based in Lancaster Medical School, at Lancaster University. Our research focus is on statistical and epidemiological methods and their implementation in open-source software. We have long-standing expertise in longitudinal, spatial and spatio-temporal methods, and growing expertise in infectious disease modelling, design of field studies, statistical genetics and computationally efficient methods for high-dimensional data. CHICAS also has a special focus on the spatial epidemiology of diseases in developing country settings. Some of the current projects are: river-blindness prevalence mapping in Africa; eco-epidemiology of Leptospirosis in the urban slums of Brazil; historical mapping of malaria Africa-wide; Amazonian food security. Over the years, CHICAS has been directing its efforts for capacity-building in Africa through training of PhD students and delivery of courses in local institutions.
Predicting Vulnerability and Improving Resilience of the Maasai Communities to Vector-Borne Infections: An Ecohealth Approach in the Maasai Steppe Ecosystem
Paul S. Gwakisa, MSc, PhD1,2; Mary Simwango, BVM1; Happiness Nnko, BSc, MSc2; Anibariki Ngonyoka, BSc, MSc2; Linda P. Salekwa, BSc, MSc1; Moses Ole-Neselle, BVM, MSc1; Anna Estes, BA, MSc, PhD2,3; Isabella Cattadori, BS, PhD3; Peter Hudson, BSc, DPhil, FRS, CoFRSE3
1Sokoine University of Agriculture, PO Box 3019, Morogoro, Tanzania;
2The Nelson Mandela African Institution of Science and Technology, PO Box 447, Arusha, Tanzania;
3The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, United States of America
Trypanosomiasis, transmitted by tsetse flies, is an endemic disease in northern Tanzania where it affects humans and animals. Distribution of tsetse flies is profoundly affected by changes in climate and land use/cover. These changes are exacerbated in the Maasai steppe by close interactions between humans, domestic and wild animals. We have shown that tsetse fly abundance varies with season and temperature, with G. swynertonni and G. m. morsitan abundance peaks differing from G. pallidipes peaks. Further, there was limited relationship between fly species abundance and temperature variation. The generalised linear mixed (GLMM) effect model indicated siginificant negative relationship between maximum temperature and vector abundance across habitats. The highest tsetse catches were recorded in the woodland-swampy ecotone habitat and lowest in riverine, where G. pallidipes was significantly abundant. Molecular analysis of over 4500 tsetse flies over a period of 15 months revealed a 5.6% overall prevalence of trypanosome infections and prevalence varied by season and location. The most prevalent trypanosome species was T. vivax while T. congolense and T. brucei were least abundant. DNA sequencing of blood meals from caught tsetse flies revealed a diversity of hosts including ostrich, buffalo and humans. Further analysis of 1000 cattle DNA samples revealed a prevalence of 17.2% with 5% of these being T. brucei infections, which could be human infective. We are currently developing an ecohealth partnership on trypanosomiasis control through collaboration with Maasai opinion leaders and early adopters in order to reduce vulnerability and enhance community resilience.
Increasing Resilience to Malaria and Schistosomiasis from an Ecohealth perspective on the Sahel Belt (Côte d’ivoire and Mauritania)
Brama Kone, Eng, PhD1,2, Mouhamadou Chouaïbou, PhD2, Sid’Ahmed Dahdi, MD,MSc3, Dieudonné K. Silue, PhD2,4, Emmanuel L.J-C. Esso, PhD2,4, Yves N. Tian-Bi, PhD2,4, Gilbert Fokou, PhD2, Hampaté Bâ, PhD5, Moussa Keita, PhD3,5, Ousmane Bâ, PhD3,5, Ibrahima Sy, PhD6,7, Grégoire Y. Yapi, PhD8, Emmanuel Tia, PhD8, Mohamed Dosumbia, PhD2,4, Tanoh A.S.R. Nkrumah, MSc2,4, Constant Gbalegba, MSc2,9, Richard K. M’bra, MSc2,4, Jeanne-d’Arc Koffi, MSc2,4, Aboudramane Kaba, MSc2,4, Honorate Ballé, BS2,4, Moussokoro Sidibé, MSc3, Cheikh M. Seyed, BS3, Giovanna Raso, PhD7, Benjamin G. Koudou, PhD10,
- University Peleforo Gon Coulibaly, Institut de Gestion Agropastorale, Korhogo, Côte d’Ivoire
- Centre Suisse de Recherches Scientifiques en Côte d’Ivoire
- University of Sciences and Technologies of Medicine, Nouakchott, Mauritania
- University Félix Houphouet Boigny, Abidjan, Côte d’Ivoire
- Institut National de Recherches en Santé Publique , Nouakchott, Mauritanie
- Centre de Suivi Ecologique, Dakar, Senegal
- Swiss Tropical and Public Health Institute, Bâle, Switzerland
- Centre d’Entomologie Medicale et Veterinaire, Abidjan, Côte d’Ivoire
- University Nanguy Abrogoua, Abidjan, Côte d’Ivoire
- Lyverpool School of Tropical Medecine, Lyverpool, United Kingdom
Ecohealth methodology was implemented in two cities of the Sahel belt to understand the complexity of hazards, vulnerabilities and exposures to malaria and schistosomiasis and identify sustainable solutions for resilience.
In each city, following a multi-stakeholders’ engagement process, two cross-sectional surveys were done in dry and rainy seasons, namely, household questionnaire, blood, feces and urine analysis, entomological, malacological and geographical surveys. Additionally, weather and climate data were generated. Analysis is ongoing.
In Korhogo (Côte d’Ivoire), schistosomiasis prevalence was 0.4 % (10/2373) and 4.6 %(102/2211) for respectively urinary and intestinal forms. In Kaedi (Mauritania) the prevalence of urinary schistosomiasis was 4% (86/2162) and statistically higher in the dry season (χ2=5.64; p = 0.017). Only one case of intestinal schistosomiasis was observed. Bulinus truncatus, B. forskalii, Biomphalaria Pfeifferi, and B. senegalensis were collected. Malaria prevalence was 12.5 % (863/6868) and 0.3 % (26/8159) respectively in Korhogo and Kaedi with a predominance of P. falciparum. Dissolved oxygen has a positive significant correlation with the presence of Anopheles gambiae larvae (OR =1.20; p=0.029). Rainfall of the preceding two month was associated to an increase of malaria incidence of 0.9% to 1%. The most important assets of communities to face the diseases are the individual and social capitals. Support from national public services and non-public services do not appear to be a major asset for resilience.
Preliminary results are proving usefulness of the methodology. Ecohealth could then be a spearhead for sustainable adaptation of malaria and schistosomiasis-affected communities to climate change and/or variability.
Towards a ZIKV Climate-Health Service at the Latin American Observatory
Ángel G. Muñoz, PhD1,2,3, Xandre Chourio3, Madeleine C. Thomson, PhD2,4,5, Anna Stewart, PhD6,3, Patricia Nájera7, Rémi Cousin2
1Atmospheric and Oceanic Sciences (AOS)/Geophysical Fluid Dynamics Laboratory (GFDL). Princeton University. New Jersey. United States of America.
2International Research Institute for Climate and Society (IRI). Earth Institute. Columbia University. New York. United States of America.
3Latin American Observatory for Climate Events. Centro de Modelado Científico (CMC). Universidad del Zulia. Venezuela.
4 Mailman School of Public Health Department of Environmental Health Sciences. Columbia University. New York. United States of America.
5 WHO Collaborating Centre (US 306) on Early Warning Systems for Malaria and other Climate Sensitive Diseases. United States of America.
6 Center for Global Health and Translational Science. Upstate Medical University. New York. United States of America.
7 IHR, Epidemic Alert and Response, and Water Borne Diseases. Communicable Diseases and Health Analysis. Pan American Health Organization (PAHO)/World Health Organization (WHO). Washington DC. United States of America.
The World Health Organization declared the Zika virus (ZIKV) a Public Health Emergency of International Concern (PHIEC) in February 2016 after a cluster of microcephaly cases and other neurological disorders potentially linked to Zika virus were observed. In response, the Latin American Observatory —an informal regional partnership involving more than 15 countries, aimed at improving climate-smart decision-making services— joined efforts with health practitioners and allied research institutes to design and co-produce a set of information tools to help fight the Zika epidemic in Latin America and the Caribbean.
Using the Datoteca, a local branch of the International Research Institute for Climate and Society’s (IRI) Data Library, the Observatory is developing a web service that (a) fully considers the contribution of climate as a partial driver of the disease seasonality, (b) uses the most updated health and socio-economic information relevant to the problem, and (c) integrates these different components in an action-oriented, easy-to-use and freely-available online interface that permits the users to assess both present and expected conditions that could affect ZIKV-sensitive populations in the Americas. Here we introduce and discuss the first version of this novel climate-health service, available —in Spanish— at http://datoteca.ole2.org/maproom/Sala_de_Salud-Clima/index.html.es.
The Impact of Climate on the Current and Future Prevalence of the Ae. aegypti Vector in Brownsville, Texas
Kelly L. Neely, BS, and Jennifer Vanos, PhD
Texas Tech University, Lubbock, Texas, United States
Zika, Chikungunya, and Dengue pose a serious health threat to many areas of North and South America, and are spreading into regions that have had no prior exposure to these viruses. The most recent human health risk involves the relationship between Zika during pregnancy and microcephaly in newborns. The Aedes (Ae.) aegypti vector transmits all three of these viruses. Additionally, air temperature, relative humidity, and rainfall have all been shown to affect the development and survival of this mosquito, which is present in a large swath of the southeastern and south central United States, including Brownsville, Texas.
This study assesses the effect of a changing climate on the prevalence of the Ae. aegypti vector. Population modeling is accomplished using the Skeeter Buster model, which uses temperature, rainfall, and relative humidity inputs as well as field-based neighborhood housing/mosquito container data from Brownsville collected in the spring of 2015. The model outputs a dynamic and spatially explicit representation of Ae. aegypti populations. Downscaled climate data for Brownsville are used to model the influence of climate to the year 2100, under two scenarios (RCP8.5 [increasing emissions] and RCP4.5 [stabilizing emissions]) .Within each scenario, six climate models are utilized. Results demonstrate a lengthening of the vector seasonality in Brownsville and increasing populations of parous females, which are a greater indicator of disease risk. Results will provide the Brownsville Public Health Department with information that can be used to help mitigate the plausible threat that Zika, Chikungunya, and Dengue viruses may pose.
TDR-IDRC Research Initiative: Population Health Vulnerabilities to Vector-Borne Diseases – Increasing Resilience under Climate Change Conditions in Africa
Vectors, Environment and Society, Special Programme for Research and Training in Tropical Diseases, World Health Organization, Geneva, SWITZERLAND; and the International Development Research Centre, Ottawa, Ontario, CANADA
Lack of knowledge on the possible impacts of climate change on vector-borne diseases (VBD) in Africa remains a serious obstacle to evidence-based health policy change. Thus, the importance of research in the area of transmission dynamics and the disease burden of VBDs along with an understanding of the complex interaction of a plurality of factors within a socio-ecological system that is under pressure from climate change.
The overall goal of the TDR-IDRC Research Initiative is to generate evidence through research projects that will enable the development of innovative strategies to reduce VBD-related human vulnerability and to increase resilience of African populations to such VBD-related health threats. In addition, it will result in knowledge, research capacity, collaboration and policy advice products that can be used throughout Africa and other regions. Capacity will be built to ensure that researchers and communities will have the know-how to generate and use the evidence necessary to reduce population health vulnerabilities in a sustainable manner.
The five research projects are as follows:
- Social, environmental and climate change impact of vector-borne diseases (malaria and schistosomiasis) in arid areas of Southern Africa (Botswana, Zimbabwe and South Africa)
- Early warning systems for improved human health and resilience to climate sensitive vector-borne diseases (malaria and Rift Valley fever) in Kenya
- Predicting vulnerability and improving resilience of the Maasai communities to vector-borne infections (trypanosomiasis): an ecohealth approach in the Maasai Steppe ecosystem (Tanzania)
- Human African trypanosomiasis: alleviating the effects of climate change through understanding human-vector-parasite interactions (Tanzania, Zimbabwe)
- Vulnerability and resilience to malaria and schistosomiasis in northern and southern fringes of the Sahelian belt in the context of climate change (Cote d’Ivoire and Mauritania)
(Acknowledgements: The International Research Institute on Climate and Society at Columbia University, New York, USA; WHO Department of Public Health, Environmental and Social Determinants of Health, Geneva, Switzerland; Programme for the Protection of the Human Environment, WHO Regional Office for Africa; Ministries of Health and Ministries of Environment; communities; Special Project Team; research teams, consultants and facilitators)
The WWRP/WCRP Sub-Seasonal to Seasonal Prediction Project (S2S)
International Research Institute for Climate and Society, Columbia University, Palisades, NY, United States
A joint World Weather Research Programme/ World Climate Research Programme (WWRP/WCRP initiative on subseasonal to seasonal (S2S) prediction has recently been launched to foster collaboration and research in the weather and climate communities, with the goals of improving forecast skill and physical under-standing, promoting forecast uptake by operational centres, and exploitation by the applications community. A key component of the
project is to create an archive of sub-seasonal operational forecasts from global producing centres (GPCs), to facilitate research and development of forecast products and solutions for early warning and managing weather risks on the time scale from 2 weeks to a season. This database became operational at ECMWF in 2015, and now includes an archive of forecasts (3 weeks behind real time), and reforecasts from 9 GPCs. The database is currently being
reproduced at the Chinese Meteorological Agency (CMA) as a second archiving centre.
An important S2S goal is to capitalize on the expertise of the weather and climate research communities to address issues of importance to the Global Framework for Climate Services, and early warning for weather/climate sensitive diseases is a key area of interest. Potential examples include vector-borne diseases such as malaria, enhanced risk of water-borne diseases like cholera due to flooding, and early warning of heat/cold waves. The S2S database provides researchers with an exciting new opportunity to investigate the predictability of underlying weather and climate conditions, demonstrate skill in the latest forecasting systems, and to develop tailored early warning information for public health decision makers.
Climate Variability and Malaria: a case of Punjab, Pakistan
Sobia Rose, PhD Scholar1, Muhammad Faisal Ali PhD Scholar1,2, Muhammad Ashfaq, PhD1,
Khuda Bakhsh, PhD3
1 University of Agriculture, Faisalabad, Pakistan
2 New York University, New York, United States of America
3 Comsats Institute of Information Technology, Vehari, Pakistan
Relation between changing climate and human health is a little complicated when we consider infectious diseases like malaria which is a second most prevalent disease in Pakistan. Despite many malaria eradication programs the disease burden is still there so the main objective of the study is to determine the spacio-temporal effects of climate change on malarial morbidity including socio-economic conditions in the 15 districts of Province Punjab. Linkages between climate change, socio economic conditions and malaria were investigated on monthly basis from 2000 to 2013. The stepwise model selection procedure was adopted and Generalized Linear Model with negative binomial family was applied. Findings revealed that temperature, rainfall and humidity have significant relationship as two months lag to the malaria month of interest and their quadratic terms also showed significant but negative relationship with the malaria prevalence. Education of the females was negative and significant while health facility showed a positive and highly significant relation with malaria prevalence which was contrary to a priori expectations. As increase in only the number of health facilities increases the disease reporting but if there is no improvement in the quality of the health services it will cause no reduction in the malaria burden. Malaria prevalence is different from one district to the other because of climatic variability so a targeted health intervention is required considering the climatic conditions of the districts. Better education of the females and a better quality health services are also needed to make these areas malaria free.
A Roadmap to Early Warning Systems for Climate Sensitive Diseases in Tanzania: Demonstrating Effect of Extreme Climate Events on Malaria Burden
Susan F. Rumisha, PhD1 and Frank Chacky, MSc2
1National Institute for Medical Research, P.O. Box 9653, Dar es Salaam, Tanzania;
2 National Malaria Control Program, Ministry of Health, Community Development, Gender, Elderly & Children, P.O. Box 9083, Dar es Salaam, Tanzania
Tanzania was affected strongly by extreme climate event of El-Niño that occurred in 1997/1998. A number of districts experienced malaria epidemics which were highly associated with the event. The El-Niño impacted the climate of the region again in 2015. Recently, the Tanzania Meteorological Agency (TMA) launched a new climate service which offers access to over 30-years of gridded rainfall and temperature data. This work analyzed malaria seasonality and demonstrate contribution of extreme climate events in malaria burden. Understanding these patterns can facilitate establishment of early warning systems for outbreaks, and strengthen capacity to forecast, promptly respond and manage outbreaks. A 20-years data of malaria cases from three districts were analyzed in the light of Oceanic Niño Index to detect patterns of malaria cases that might be associated with El-Niño Southern Oscillation (El-Niño and La-Niña). Two districts, (Babati and Dodoma) experienced malaria epidemic in 1998/1999. The third district, Iringa Rural, did not report outbreak on that time. Malaria cases increased during moderate and very strong El-Niño and La-Niña, in some instance, during weak index. Peak malaria season for Dodoma and Iringa is March/May, while in Babati is holoendemic with slight peak on April/May. During abnormal index levels, malaria cases went above the expected range, not only during peak times but also other months. Dodoma district was affected the most. This pilot analysis highlighted importance of studying patterns of extreme weather events in guiding effective response to malaria epidemics. Full analysis will utilize the high quality climate products from the TMA.
Long-Lead El Niño Forecast Information to Support Public Health Decision Making
Desislava Petrova MRes1 , Rachel Lowe PhD1, Anna Stewart-Ibarra PhD, MPA2, Joan Ballester PhD1, Siem Jan Koopman Prof3, Xavier Rodo PhD1,4, Xavier Rodo ́ PhD
1Catalan Institute of Climate Sciences (IC3), Barcelona, Catalonia, Spain
2SUNY Upstate Medical University, Syracuse, New York, USA
3Vrije Universiteit Amsterdam, Amsterdam, Netherlands
4Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Catalonia, Spain
El Niño-Southern Oscillation (ENSO) is a high-impact climatic phenomenon that affects weather globally. It triggers floods and droughts, damages agriculture and the economy, and increases the risk of infectious diseases in various regions. Therefore, ENSO forecasts could help authorities to plan in advance of imminent disasters, and to protect vulnerable communities.
A structural time series model with predictors relevant to the El Niño (EN) evolution has been used to predict sea surface temperature (SST) in the Niño 3.4 region. The model is tailored to forecast EN at long lead times of 2 years or more. The forecasts provide information about the amplitude, duration, and peak time of the events that could be used to support decision making in tropical countries, which are directly affected by ENSO.
For example, it was found in a previous study that the timing and magnitude of dengue outbreaks in El Oro province in Ecuador were associated with El Niño. Hence, in this study long-lead forecasts of the Niño3.4 index are used within a dengue model, to assess the extent to which dengue epidemics can be predicted well in advance. Our results show that the ENSO model could have helped to predict the dengue epidemic that occurred in the region after the 2009/10 EN as early as 24 months ahead. Thus, long-lead ENSO forecasts could be incorporated into dengue prediction models, to enhance the development of a dengue early warning system for Ecuador and other tropical and subtropical countries sensitive to ENSO.
Malaria Early Warning System for Uganda using ECMWF weather forecasts to drive a dynamical malaria model
Adrian M Tompkins1, Felipe Colon Gonzalez1,2, Francesca Di Giuseppe3, and Didas Namanya4
1 Abdus Salam International Centre for Theoretical Physics, Trieste, Italy
2 University of East Anglia, Norwich, United Kingdom
3 European Centre for Medium Range Weather Forecasts, Reading, United Kingdom
4 Ministry of Health, Kampala, Uganda
As monthly and seasonal dynamical climate prediction systems have improved their skill in the tropics over recent years, there is now the potential to drive dynamical malaria models to provide early warnings of climate-related transmission hazard. A pilot dynamical malaria prediction system is introduced. Temperature and precipitation ensemble forecasts from the ECMWF monthly/seasonal prediction systems are used to drive the spatially explicit, dynamical malaria model VECTRI. The 4-month lead time malaria forecasts are initialized with vector density and malaria prevalence levels derived from climate observations. The parameters predicted are the entomological inoculation rate (EIR), parasite ratio and in the latest version, expected clinical cases. Forecasts are made on a 25km grid mesh aggregated at the administrative district level, normalized, and then evaluated using normalized district level crude incidence data or sentinel site cases (laboratory confirmed) in Uganda. Despite an imperfect climate forecast and malaria model as well as uncertainties in the health data itself, the results are promising, with the model able to reproduce differences between sentinel sites over the short data record available, and significantly skill at the district level for half of Uganda. This is true for districts of both low and higher endemicity, although the system works best in epidemic zones where climate can be a strong driver of inter-annual variability. The next step of introducing the malaria model calibration using a new sequential Monte Carlo constrained genetic algorithm will be discussed.
Constraining the Relative Uncertainty of Malaria Simulations due to Climate Spatial Heterogeneity and Dynamical Malaria Model Parameter Specification
Adrian M Tompkins1, Madeleine Thomson2
1 Abdus Salam International Centre for Theoretical Physics, Trieste, Italy
2 International Research Institute for Climate and Society, New York, United States of America
This work assesses the origin of uncertainty when simulating malaria transmission variability due to climate variability and trends with dynamical malaria models. While acknowledging the uncertainty in the health data itself, simulation errors are also due to measurement error and spatial representativeness of the driving climate data, in addition to the inaccurate specification dynamical malaria model process-related parameters. To ascertain the relative importance of climate versus malaria model uncertainty for a highland location, experiments simulating malaria transmission for Kericho are made using the VECTRI dynamical model for falciparum transmission that accounts for hydrology, temperature, population density and immunity. In each experiment the model is calibrated using a sequential Monte Carlo constrained genetic algorithm, in which the constraint refers to an additional cost function due to climate or model parameter departures from their default value, normalized by an estimate of the parameter uncertainty. This constraint prevents the system from over-fitting malaria data by specifying unrealistic parameter settings or climate anomalies. Two experiments are performed, in which either the climate (from station or gridded products) or the key malaria model parameters are permitted to evolve. Modeling the climate error that represents the spatial heterogeneity that may occur within a catchment area leads to the best fit to the data, despite the fact that more than 15 malaria model variables were allowed to evolve in the second experiment. This emphasizes the importance of accounting for heterogeneity in rainfall and temperature in spatially explicit simulations.
Use of Vectorial Capacity in Describing and Forecasting of Malaria Cases in Kericho, Kenya
Israel Ukawuba, MPH1, Madeleine C Thomson, PhD1, and Peter J Diggle, PhD2
1Columbia University, International Research Institute for Climate and Society, New York, New York, United States;
2Lancaster University, Lancaster, United Kingdom
Temperature and rainfall play a key role in mosquito vector abundance and, as a result are crucial to malaria disease transmission. Over the last decade, climate-integrated malaria intervention techniques in Africa have been widely overlooked; however, providing evidence of the impact of interventions on malaria decline, as opposed to other factors such as climate, is becoming increasingly important in ensuring continued political and financial support. Therefore in this study, our objective is to show that a rainfall and temperature driven model of vectorial capacity (VCAP) -the potential for vector-borne disease transmission in humans- can describe and predict malaria transmission. In doing so, we aim to indicate the role of climate in the kinetics of malaria transmission. We used an Autoregressive Integrated Moving Average model (ARIMA 1,0,1) to fit monthly malaria cases from a tea estate in Kericho, Kenya. The relationship of VCAP to log-malaria incidence was strongest two months prior (R = 0.42). Likelihood ratio test of nested models identified the full model as a better fit than the restricted model (p-value< 0.05, DF=1, X2 = 12.03). VCAP-based malaria predictions showed strong correlation to malaria incidence (R=0.62) and successful 2-month lead forecast of malaria outbreaks following the El Niños of 1990, 1997/1998, and 2002. Of the 86 monthly epidemic warnings from 1989 to 2004, 61 were correctly identified and 25 were falsely identified. The results obtained here, could be used to 1) assess the potential impact of climate on malaria transmission and 2) predict epidemics following periods of unusually favorable climate.