ArticleThe American journal of tropical medicine and hygiene2014
Implementation of malaria dynamic models in municipality level early warning systems in Colombia. Part I: description of study sites.
Article in The American journal of tropical medicine and hygiene, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Time lag effect on malaria transmission dynamics in an Amazonian Colombian municipality and importance for early warning systems.Scientific reports · 2023Article
- Time-Varying Effects of Meteorological Variables on Malaria Epidemiology in the Context of Interrupted Control Efforts in the Amazon Rainforest, 2000-2017.Frontiers in medicine · 2021Article
- Effects of local and regional climatic fluctuations on dengue outbreaks in southern Taiwan.PloS one · 2017Article
- Towards malaria control and elimination in Ghana: challenges and decision making tools to guide planning.Global health action · 2017Article
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Authors and funding
11 authors.
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Abstract
As part of the Integrated National Adaptation Pilot project and the Integrated Surveillance and Control System, the Colombian National Institute of Health is working on the design and implementation of a Malaria Early Warning System framework, supported by seasonal climate forecasting capabilities, weather and environmental monitoring, and malaria statistical and dynamic models. In this report, we provide an overview of the local ecoepidemiologic settings where four malaria process-based mathematical models are currently being implemented at a municipal level. The description includes general characteristics, malaria situation (predominant type of infection, malaria-positive cases data, malaria incidence, and seasonality), entomologic conditions (primary and secondary vectors, mosquito densities, and feeding frequencies), climatic conditions (climatology and long-term trends), key drivers of epidemic outbreaks, and non-climatic factors (populations at risk, control campaigns, and socioeconomic conditions). Selected pilot sites exhibit different ecoepidemiologic settings that must be taken into account in the development of the integrated surveillance and control system.
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