Mapping the endemicity and seasonality of clinical malaria for intervention targeting in Haiti using routine case data

Author:

Cameron Ewan12ORCID,Young Alyssa J34,Twohig Katherine A5,Pothin Emilie36,Bhavnani Darlene3,Dismer Amber7,Merilien Jean Baptiste8,Hamre Karen9,Meyer Phoebe3,Le Menach Arnaud3,Cohen Justin M3,Marseille Samson810,Lemoine Jean Frantz8,Telfort Marc-Aurèle8,Chang Michelle A9,Won Kimberly9,Knipes Alaine9,Rogier Eric9,Amratia Punam5,Weiss Daniel J12,Gething Peter W12,Battle Katherine E11

Affiliation:

1. Curtin University, Perth, Australia

2. Telethon Kids Institute, Perth Children’s Hospital, Perth, Australia

3. Clinton Health Access Initiative, Boston, United States

4. Tulane University School of Public Health and Tropical Medicine, New Orleans, United States

5. Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom

6. Swiss Tropical and Public Health Institute, Basel, Switzerland

7. Division of Global Health Protection, Centers for Disease Control and Prevention, Atlanta, United States

8. Programme National de Contrôle de la Malaria/MSPP, Port-au-Prince, Haiti

9. Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, United States

10. Direction d’Epidémiologie de Laboratoire et de la Recherche, Port-au-Prince, Haiti

11. Institute for Disease Modelling, Seattle, United States

Abstract

Towards the goal of malaria elimination on Hispaniola, the National Malaria Control Program of Haiti and its international partner organisations are conducting a campaign of interventions targeted to high-risk communities prioritised through evidence-based planning. Here we present a key piece of this planning: an up-to-date, fine-scale endemicity map and seasonality profile for Haiti informed by monthly case counts from 771 health facilities reporting from across the country throughout the 6-year period from January 2014 to December 2019. To this end, a novel hierarchical Bayesian modelling framework was developed in which a latent, pixel-level incidence surface with spatio-temporal innovations is linked to the observed case data via a flexible catchment sub-model designed to account for the absence of data on case household locations. These maps have focussed the delivery of indoor residual spraying and focal mass drug administration in the Grand’Anse Department in South-Western Haiti.

Funder

Bill and Melinda Gates Foundation

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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