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Background <p><i>Plasmodium falciparum</i> and <i>Plasmodium vivax</i> cause most malaria cases worldwide and are co-endemic in many countries, yet differ substantially in biology and in their responses to common interventions. As programmes drive down <i>P. falciparum</i>, <i>P. vivax</i> is a growing challenge for elimination, but most modelling tools assess the species separately, limiting coordinated policy. We aimed to build a unified malaria transmission modelling framework for co-endemic settings and assess how well it reflects global prevalence.</p> Methods <p>We integrated an established <i>P. vivax</i> model into a flexible <i>P. falciparum</i> modelling platform, enabling parallel simulation of both species within a shared biological, demographic, and intervention environment. Modelled equilibrium prevalences, matched by mosquito density, were compared with 19,225 yearly co-prevalence estimates from the Malaria Atlas Project (769 sub-national regions, 33 co-endemic countries, 2000–2024); uncertainty was represented by 95% quantile-based regions from 50 parameter draws. We assessed how this fit was modified by biological factors and simulated interventions.</p> Results <p>Here we show that the framework captures 51% of co-prevalence estimates within its uncertainty regions, rising to 65.5% when country-specific <i>P. vivax</i> relapse rates and human Duffy negativity are included. <i>P. falciparum</i> predominates where mosquito densities are high, whereas <i>P. vivax</i> is relatively more prevalent at lower densities. Simulated interventions produce larger relative reductions in <i>P. falciparum</i> prevalence, while <i>P. vivax</i> shows greater rebounds after intervention withdrawal, particularly at low mosquito densities.</p> Conclusions <p>This unified framework provides a quantitative tool to support coordinated, species-specific intervention strategies in co-endemic settings, a step toward sustainable malaria elimination.</p>

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Integrating parallel Plasmodium falciparum and Plasmodium vivax malaria models in a unified framework to capture co-endemic prevalence patterns

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月7日

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  • Richard J. Sheppard,
  • Giovanni D. Charles,
  • Constanze Ciavarella,
  • Nora Schmit,
  • Shazia N. Ruybal-Pesántez,
  • Gina Cuomo-Dannenburg,
  • Tom R. Brewer,
  • Michael T. White,
  • Peter Winskill

摘要

Background

Plasmodium falciparum and Plasmodium vivax cause most malaria cases worldwide and are co-endemic in many countries, yet differ substantially in biology and in their responses to common interventions. As programmes drive down P. falciparum, P. vivax is a growing challenge for elimination, but most modelling tools assess the species separately, limiting coordinated policy. We aimed to build a unified malaria transmission modelling framework for co-endemic settings and assess how well it reflects global prevalence.

Methods

We integrated an established P. vivax model into a flexible P. falciparum modelling platform, enabling parallel simulation of both species within a shared biological, demographic, and intervention environment. Modelled equilibrium prevalences, matched by mosquito density, were compared with 19,225 yearly co-prevalence estimates from the Malaria Atlas Project (769 sub-national regions, 33 co-endemic countries, 2000–2024); uncertainty was represented by 95% quantile-based regions from 50 parameter draws. We assessed how this fit was modified by biological factors and simulated interventions.

Results

Here we show that the framework captures 51% of co-prevalence estimates within its uncertainty regions, rising to 65.5% when country-specific P. vivax relapse rates and human Duffy negativity are included. P. falciparum predominates where mosquito densities are high, whereas P. vivax is relatively more prevalent at lower densities. Simulated interventions produce larger relative reductions in P. falciparum prevalence, while P. vivax shows greater rebounds after intervention withdrawal, particularly at low mosquito densities.

Conclusions

This unified framework provides a quantitative tool to support coordinated, species-specific intervention strategies in co-endemic settings, a step toward sustainable malaria elimination.