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Malaria Control: Epidemic Progression Calculation Based on Individual Mobility Data

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Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2020)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 306))

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Abstract

Malaria remains an endemic disease in many developing countries around the world. For several decades, various malaria control strategies have been developed and implemented in many countries. Many fields like medicine, biology, physics, mathematics, and computer sciences are used to set up these strategies. Mathematical models have proven to be very useful in understanding malaria progression and dynamics. Mainly, these models are based on differential equations and take into account the clinical and biological characteristics of patients and vectors. They made it possible to study the parts of the population which pass from a state of exposure to the disease to different possible states as a result. These mathematical models in general consider global estimates of the evolution of the epidemic based on unspecified percentages of the population. Individuals are considered fairly evenly. However, the health status of each individual as well as their own daily activities can have a particular influence on the epidemic evolution. The objective of this paper is to propose a model based on a daily evolution of the individual’s condition, which depends on their mobility and the characteristics of the territory they visit. Thus, mobility data of a single person moving from one area to another is used to calculate disease progression and predict outcomes. We implement our solution and demonstrate its effectiveness through empirical experiments. The results show how promising the model is in providing possible information on the failure of disease control in the Kédougou region.

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Notes

  1. 1.

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Author information

Authors and Affiliations

  1. Ecole Polytechnique de Thiès, LTISI, Thies, Senegal

    Ibrahima Gueye

  2. Sorbonne Université, Laboratoire d’Informatique de Paris 6, LIP6, 75005, Paris, France

    Hubert Naacke, Lynda Bouzid Khiri & Stéphane Gancarski

  3. Department of Mathematics and Computer Science, Cheikh Anta Diop University, Dakar - Fann BP, 5005, Senegal

    Idrissa Sarr

Authors
  1. Ibrahima Gueye
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  2. Hubert Naacke
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  4. Lynda Bouzid Khiri
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  5. Stéphane Gancarski
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Correspondence to Ibrahima Gueye .

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Editors and Affiliations

  1. KASIT, University of Jordan, Amman, Jordan

    Mohammad S. Obaidat

  2. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Tuncer Oren

  3. University of Calabria, Rende, Cosenza, Italy

    Floriano De Rango

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Gueye, I., Naacke, H., Sarr, I., Khiri, L.B., Gancarski, S. (2022). Malaria Control: Epidemic Progression Calculation Based on Individual Mobility Data. In: Obaidat, M.S., Oren, T., Rango, F.D. (eds) Simulation and Modeling Methodologies, Technologies and Applications. SIMULTECH 2020. Lecture Notes in Networks and Systems, vol 306. Springer, Cham. https://doi.org/10.1007/978-3-030-84811-8_8

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