Modelling the epidemiology of residual Plasmodium vivax malaria in a heterogeneous host population: a case study in the Amazon Basin

Abstract

15 The overall malaria burden in the Americas has decreased dramatically over the past two 16 decades, but residual transmission pockets persist across the Amazon Basin, where 17 Plasmodium vivax is the predominant infecting species. Current elimination efforts require a 18 better quantitative understanding of malaria transmission dynamics for planning, monitoring, 19 and evaluating interventions at the community level. This can be achieved with mathematical 20 models that properly account for risk heterogeneity in communities approaching elimination, 21 where few individuals disproportionately contribute to overall malaria prevalence, morbidity, 22 and onwards transmission. Here we analyse demographic information combined with 23 routinely collected malaria morbidity data from the town of Mâncio Lima, the main urban 24 transmission hotspot of Brazil. We estimate the proportion of high-risk subjects in the host 25 population by fitting compartmental susceptible-infected-susceptible (SIS) transmission 26 models simultaneously to age-stratified vivax malaria incidence densities and the frequency 27 distribution of P. vivax malaria attacks experienced by each individual over 12 months.28 Simulations with the best-fitting SIS model indicate that 20% of the hosts contribute 86% of 29 the overall vivax malaria burden. Despite the low overall force of infection typically found in 30 the Amazon, about one order of magnitude lower than that in rural Africa, high-risk individuals 31 gradually develop clinical immunity following repeated infections and eventually constitute a 32 substantial infectious reservoir comprised of asymptomatic parasite carriers that is overlooked 33 by routine surveillance but likely fuels onwards malaria transmission. High-risk individuals 34 therefore represent a priority target for more intensive and effective interventions that may 35 not be readily delivered to the entire community. 36 3 37 Keywords: Mathematical modelling, urban malaria, heterogeneity, Amazon, hotspots, 38 asymptomatic infection. 39 4 40 Introduction 41 Heterogeneity in the risk of infection with several pathogens has been repeatedly documented 42 in human populations, with 20% of the hosts typically harbouring 80% of the pathogen burden 43 in the community [1]. For example, residents in the same village in rural Africa may greatly 44 differ in their malaria risk, leading to over-dispersed frequency distributions of malaria attacks 45 per person over time, with few subjects in the community experiencing frequent infection and 46 disease [2]. 4748 One source of malaria risk heterogeneity is the varying hosts' exposure to the pathogen, which 49 can be measured as the number of infectious mosquito bites per host per unit of time, termed 50 the entomological inoculation rate (EIR). About 20% of the children are estimated to receive 51 80% of all infectious mosquito bites in rural African settings, suggesting that malaria parasites 52 may also conform to the "20/80 rule" [3]. Significant malaria risk heterogeneity has also been 5...

Monitoring Plasmodium vivax resistance to antimalarials: Persisting challenges and future directions

“…Because high-risk individuals tend to be infected earlier during the post-treatment follow-up, they are selectively removed from the susceptible population and those who remain uninfected are at low risk of infection, due to limited exposure or acquired immunity. As a consequence, the incidence of new cases gradually decreases over time during the follow-up, violating the constant rate assumption ( Corder et al, 2020b ).…”

“…This is due to the substantial risk heterogeneity in endemic populations, which affects the dynamics of new infections in time-to-event analysis. Typically, 20% of the exposed individuals contribute over 80% of the overall P. vivax malaria burden (e.g., Corder et al, 2020b ). Because high-risk individuals tend to be infected earlier during the post-treatment follow-up, they are selectively removed from the susceptible population and those who remain uninfected are at low risk of infection, due to limited exposure or acquired immunity.…”

Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold

“…Curves generated with the model (Equation 1) with gamma distributed susceptibility (black) or connectivity (gray) assuming R 0 = 3: (solid) herd immunity threshold; (dashed) final size of uncontrolled epidemic. Vertical lines indicate coefficients of individual variation for several infectious diseases according to literature: (solid green) susceptibility or exposure to malaria [Amazon 1.79 ( 6 ), Africa 2.05 ( 7 )]; (solid blue) susceptibility or exposure to tuberculosis [Portugal 2.37, Brazil 3.33 ( 8 )]; (dotted red) infectiousness for SARS-CoV-1 [Singapore 2.62, Beijing 2.64 ( 9 )]; (dotted black) infectiousness for SARS-CoV-2 [3.22 ( 10 )].…”

Host movement, transmission hot spots, and vector-borne disease dynamics on spatial networks

“…Human mobility data are being collected from diverse sources, including traffic patterns, cell phone data, airflow, navigation app use, social media posts, and more. Detailed data are being collected as well on host demography [90] and vector habitat suitability [91,92]. Public repositories will facilitate research on integrating network and local transmission data for understanding disease dynamics.…”