Predicting Malaria Epidemics in Ethiopia

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Allan Leonard
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1 Predicting Malaria Epidemics in Ethiopia Eskindir Loha, Hawassa University Torleif Markussen Lunde, UoB 5th Conference on Global Health and Vaccination Research: Environmental Change and Global Health Tromsø 6-8 June, 2010 Addis Ababa University School of Public Health Aklilu Lemma Institute of Pathobiology National Meteorological Agency of Ethiopia University of Bergen Centre for International Health Geophysical Institute Bjerknes Centre for Climate Research Arba Minch University

2 Motivation 1. Malaria is a major public health problem in Ethiopia 2. The spread of malaria is sensitive to weather 3. Weather is predictable, so is malaria predictable? 4. Malaria prediction could be useful in making efficient use of the limited resources for malaria control.

3 Malaria forecasting? Long-term epidemic forecasting Malaria epidemic early warning: based on surveying transmission risks related to abnormal rainfall or temperature Epidemic early detection

4 Overview System components Mosquito module Epidemiological module Hydrological module Meteorological module Malaria model Preliminary results Long-term forecasting Epidemic early detection

5 Mosquito module The main malaria vector in Ethiopia is Anopheles arabiensis. Early development (egg, larva, pupa) is dependent on water temperature Adult mortality is dependent on temperature and humidity Egg laying behavior is dependent on eg. the availability of small water pools

6 Mosquito module Collection of eggs, larva and pupa Typical breeding site for Anopheles arabiensis

7 Mosquito module - Monitoring mosquito densities in villages - Monitoring breeding sites (egg, larva, pupa) Species composition Survival from egg to pupa Resting behavior Feeding behavior

8 Epidemiological module Same locations as mosquito collection Find out the prevalence and incidence of malaria in the study areas Examine the relative importance of malaria as a cause of morbidity and mortality To assess the potential epidemiological risk reasons for malaria epidemics. To examine the health seeking behavior of the people on malaria

9 Epidemiological module Each household is followed for two years Chano households (weekly basis) Butajira (survey every 3 months) households from semi-low-altitude from mid-altitude households at high-altitudes Evaluation of clinical and parasitological response to Artemether-lumefantrine (children)

10 Hydrological module

11 Meteorological module Two main projects: 1. Where does the water come from? 2. Dynamical downscaling - seasonal weather forecasts.

12 Where does the water come from?

13 Where does the water come from? Previous "truth" moisture from west Knowledge helpful in seasonal weather forecasts

14 Seasonal forecasts Limitations: 1. The predictability of where rainfall will fall down is low 2. Model suffers from this, and main goal is to reproduce whether a season is wetter or dryer than the normal (compared to stations) 3. Limited computational power and storage means clouds can not be fully resolved at the moment , based on monthly sums. In the rainy season, a lot of rain one place does not mean a lot of rain in the neighboring station.

temperature Accumulated rainfall Relative humidity

15 Seasonal forecasts Produce high space-time resolution data on: Temperature Rainfall Soil moisture Soil temperature Accumulated rainfall Relative humidity etc... Which can be used as input in malaria model Temperature

16 Malaria-Meteorology models Difficult to model climate-malaria relationship Mainly because non-climatic factors (drugs, socioeconomic conditions,...) can overrule the influence of climate over long time. Malaria-weather models make more sense since weather can be a driving factor when other factors are kept constant.

17 Dynamical models - hind-cast

18 Dynamical models - forecast Use predicted sea surface temp. to predict seasonal weather

19 Reproduction of rainfall Model seems to capture dry and wet years very well (averaged anomalies all stations). The example shows model data from , and corresponding station data.

20 Better than existing products? Does a better job in catching dry and wet years than the Tropical Rainfall Measuring Mission (TRMM)

21 Dynamical model - forced by model weather Next slide shows results from one dynamical model (we are currently working on many). The human component has been excluded, and only number of mosquitoes is modeled This means that biting behavior is not taken into account, and human immunity, bed net usage, new drugs etc. are not allowed to vary. The model is run for each location, but merged afterwards since the modeled weather not necessarily falls down the correct place. The model is evaluated every 6 hours.

22 Dynamical model - forced by model weather

23 Malaria-Meteorology models South Ethiopia

24 Malaria-Meteorology models Lessons o Complexity of malaria-meteorology link/models Different stat. or mathematical form Different variable and lag combination Varying data transformation forms

25 Malaria-Meteorology models Lessons o Biologic approach: less prediction power o Crudeness of models (malaria-meteorology link) questioned and local variations emphasized o Non-climatic factors: vector composition, land use, population movement, health services, immunity

26 Malaria-Meteorology models Objective Using area-specific Plasmodium falciparum malaria incidence and meteorological data, to unveil local variations of the malaria-meteorology link

27 Malaria-Meteorology models Method 35 locations ( ) Total cases (microscopically confirmed PF)= with in ave. 6.7 years, mean serial length 80 (range ) months

28 Malaria-Meteorology models Rainfall (35 locations), temperature (17 locations) and RH (3 locations) The model: Time series (SPSS 17) o Transfer function (TF) or Univariate ARIMA

29 Locations with Plasmodium falciparum incidence and meteorological data for which the models were developed Administrative regions of Ethiopia Geographic coordinates of the locations

30 Malaria-Meteorology models Result 5 models -dropped (diagnostic stat) so 30 Past Plasmodium falciparum malaria incidence o 17 locations (alone) and 4 locations (with meteorological predictors) o Seasonal ARIMA orders: altitude 1742 and above

31 Malaria-Meteorology models Meteorological data o Rainfall- 4 locations o Minimum temp.- 5 locations o Maximum o RH- temp.- 2 locations no significant effect

32 Malaria-Meteorology models Goodness of fit of models (R2 or stationary R2) o o o o o One model had negative value The rest ranged 16%-97%. Two third had above 50% Seasonal ARIMA above 60% (altitude 1742 and above) No significant correlation: R squared versus serial length or average monthly case numbers

33 Malaria-Meteorology models Model variations/similarities o o o o o o 7 locations only dependent on past incidence but no significant weather variable The rest included weather variable or took different data transformation forms. Some did not contain incidence at any AR or MA orders No apparent reiteration in line with altitude Two did not have any significant predictor 5 models failed due to Ljung-Box Q Models are different

Mean condition of 23 locations Locations Available data Model structure (ARIMA) 23 Incidence Rainfall (1,0,0)(0,1,0) Stationary Sig. variable Serial Ave. R2 and model length Incid.

34 Mean condition of 23 locations Locations Available data Model structure (ARIMA) 23 Incidence Rainfall (1,0,0)(0,1,0) Stationary Sig. variable Serial Ave. R2 and model length Incid./ description month (Jul 02Aug 07) Incidence at AR lag 1 and first order seasonal differencing Rainfall at delay 4 and num. TF order of 0 and first order seasonal differencing 2579

35 Malaria-Meteorology models Conclusion Local predictions Malaria-Meteorology link varied from place to place, so averaging- not logical or precise Past Plasmodium falciparum malaria incidence is worth considering (outweighed the impact of weather variables) A need to include non-meteorological factors Regional predictions Could give a longer lead to prepare for epidemics with limitations of not knowing the exact location

36 EMaPS Supervisors:. Ahmed Ali, Teshome Gebre-Micheal, Meshesha Balkew, Semu Ayalew Moges, Asgeir Sorteberg, Wakgari Deressa (coordinator), and Bernt Lindtjørn (coordinator) PhDs: Abebe Animut describes the malaria mosquitoes in the Butajira area. Adugna Woyessa: Describe human malaria in a Butajira area in rural south central Ethiopia. Korecha Diriba: Studies the link between large-scale climate and weather with the local weather experienced in Ethiopia. Dereje Tesfahun: Considers how ground conditions influence surface water distribution. Ellen Viste: Looks at the moisture transport and other driving forces of Ethiopian rainfall. Fekadu Masebo: describes the malaria mosquitoes in the Chano area.

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