Modeling malaria as a complex adaptive system

Marcus Janssen, W. J M Martens

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

As the resistance of the malaria parasite to antimalarial drugs continues to increase, as does that of the malarial mosquito to insecticides, the efficacy of efforts to control malaria in many tropical countries is diminishing. This trend, together with the projected consequences of climate change, may prove to exacerbate substantially the significance of malaria in the coming decades. In this article we introduce the use of an evolutionary modeling approach to simulate the adaptation of mosquitoes and parasites to the available pesticides and drugs. By coupling genetic algorithms with a dynamic malaria-epidemiological model, we derive a complex adaptive system capable of simulating adapting and evolving processes within both the mosquito and the parasite populations. This approach is used to analyze malaria management strategies appropriate to regions of higher and lower degrees of endemicity. The results suggest that adequate use of insecticides and drugs may reduce the occurrence of malaria in regions of low endemicity, although increased efforts would be necessary in the event of a climate change. However, our model indicates that in regions of high endemicity the use of insecticides and drugs may lead to an increase in incidence due to enhanced resistance development. Projected climate change, on the other hand, may lead to a limited reduction of the occurrence of malaria due to the presence of a higher percentage of immune persons in the older age class.

Original languageEnglish (US)
Pages (from-to)213-236
Number of pages24
JournalArtificial Life
Volume3
Issue number3
StatePublished - 1997
Externally publishedYes

Fingerprint

Malaria
Insecticides
Adaptive systems
Adaptive Systems
Climate change
Complex Systems
Malaria control
Modeling
Climate Change
Pharmaceutical Preparations
Drugs
Culicidae
Antimalarials
Parasites
Pesticides
Genetic algorithms
Epidemiological Model
Diminishing
Percentage
Efficacy

Keywords

  • Adaptation
  • Climate change
  • Genetic algorithms
  • Infectious diseases
  • Resistance development

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Control and Systems Engineering
  • Theoretical Computer Science

Cite this

Janssen, M., & Martens, W. J. M. (1997). Modeling malaria as a complex adaptive system. Artificial Life, 3(3), 213-236.

Modeling malaria as a complex adaptive system. / Janssen, Marcus; Martens, W. J M.

In: Artificial Life, Vol. 3, No. 3, 1997, p. 213-236.

Research output: Contribution to journalArticle

Janssen, M & Martens, WJM 1997, 'Modeling malaria as a complex adaptive system', Artificial Life, vol. 3, no. 3, pp. 213-236.
Janssen, Marcus ; Martens, W. J M. / Modeling malaria as a complex adaptive system. In: Artificial Life. 1997 ; Vol. 3, No. 3. pp. 213-236.
@article{7ef47ba759c344ed97b221d16ef62634,
title = "Modeling malaria as a complex adaptive system",
abstract = "As the resistance of the malaria parasite to antimalarial drugs continues to increase, as does that of the malarial mosquito to insecticides, the efficacy of efforts to control malaria in many tropical countries is diminishing. This trend, together with the projected consequences of climate change, may prove to exacerbate substantially the significance of malaria in the coming decades. In this article we introduce the use of an evolutionary modeling approach to simulate the adaptation of mosquitoes and parasites to the available pesticides and drugs. By coupling genetic algorithms with a dynamic malaria-epidemiological model, we derive a complex adaptive system capable of simulating adapting and evolving processes within both the mosquito and the parasite populations. This approach is used to analyze malaria management strategies appropriate to regions of higher and lower degrees of endemicity. The results suggest that adequate use of insecticides and drugs may reduce the occurrence of malaria in regions of low endemicity, although increased efforts would be necessary in the event of a climate change. However, our model indicates that in regions of high endemicity the use of insecticides and drugs may lead to an increase in incidence due to enhanced resistance development. Projected climate change, on the other hand, may lead to a limited reduction of the occurrence of malaria due to the presence of a higher percentage of immune persons in the older age class.",
keywords = "Adaptation, Climate change, Genetic algorithms, Infectious diseases, Resistance development",
author = "Marcus Janssen and Martens, {W. J M}",
year = "1997",
language = "English (US)",
volume = "3",
pages = "213--236",
journal = "Artificial Life",
issn = "1064-5462",
publisher = "MIT Press Journals",
number = "3",

}

TY - JOUR

T1 - Modeling malaria as a complex adaptive system

AU - Janssen, Marcus

AU - Martens, W. J M

PY - 1997

Y1 - 1997

N2 - As the resistance of the malaria parasite to antimalarial drugs continues to increase, as does that of the malarial mosquito to insecticides, the efficacy of efforts to control malaria in many tropical countries is diminishing. This trend, together with the projected consequences of climate change, may prove to exacerbate substantially the significance of malaria in the coming decades. In this article we introduce the use of an evolutionary modeling approach to simulate the adaptation of mosquitoes and parasites to the available pesticides and drugs. By coupling genetic algorithms with a dynamic malaria-epidemiological model, we derive a complex adaptive system capable of simulating adapting and evolving processes within both the mosquito and the parasite populations. This approach is used to analyze malaria management strategies appropriate to regions of higher and lower degrees of endemicity. The results suggest that adequate use of insecticides and drugs may reduce the occurrence of malaria in regions of low endemicity, although increased efforts would be necessary in the event of a climate change. However, our model indicates that in regions of high endemicity the use of insecticides and drugs may lead to an increase in incidence due to enhanced resistance development. Projected climate change, on the other hand, may lead to a limited reduction of the occurrence of malaria due to the presence of a higher percentage of immune persons in the older age class.

AB - As the resistance of the malaria parasite to antimalarial drugs continues to increase, as does that of the malarial mosquito to insecticides, the efficacy of efforts to control malaria in many tropical countries is diminishing. This trend, together with the projected consequences of climate change, may prove to exacerbate substantially the significance of malaria in the coming decades. In this article we introduce the use of an evolutionary modeling approach to simulate the adaptation of mosquitoes and parasites to the available pesticides and drugs. By coupling genetic algorithms with a dynamic malaria-epidemiological model, we derive a complex adaptive system capable of simulating adapting and evolving processes within both the mosquito and the parasite populations. This approach is used to analyze malaria management strategies appropriate to regions of higher and lower degrees of endemicity. The results suggest that adequate use of insecticides and drugs may reduce the occurrence of malaria in regions of low endemicity, although increased efforts would be necessary in the event of a climate change. However, our model indicates that in regions of high endemicity the use of insecticides and drugs may lead to an increase in incidence due to enhanced resistance development. Projected climate change, on the other hand, may lead to a limited reduction of the occurrence of malaria due to the presence of a higher percentage of immune persons in the older age class.

KW - Adaptation

KW - Climate change

KW - Genetic algorithms

KW - Infectious diseases

KW - Resistance development

UR - http://www.scopus.com/inward/record.url?scp=0031151558&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031151558&partnerID=8YFLogxK

M3 - Article

C2 - 9385735

AN - SCOPUS:0031151558

VL - 3

SP - 213

EP - 236

JO - Artificial Life

JF - Artificial Life

SN - 1064-5462

IS - 3

ER -