The impact of heterogeneous transmission on the establishment and spread of antimalarial drug resistance

Abstract

Despite the important insights gained by extending the classical models of malaria, other factors, such as immunity, heterogeneous biting, and differential patterns of drug use have not been fully explored due to the complexity of modeling multiple simultaneous malaria infections competing within a host. Understanding these factors is important for understanding how to control the spread of drug resistance to artemisinin which is just emerging in Southeast Asia. The emergence of resistance plays out at the population level, but is the result of competition within individuals for transmission events. Most studies of drug resistance evolution have focused on transmission between hosts and ignored the role of within-host competition due to the inherent complexity of modeling at multiple scales. To embed within-host competition in the model, we used an agent-based framework that was developed to understand how deviations from the classical assumptions of the Ross-MacDonald type models, which have been well-described and analyzed, impact the dynamics of disease. While structured to be a stochastic analogue to classical Ross-Macdonald type models, the model is nonetheless based on individuals, and thus aspects of within-host competition can be explored. We use this framework to explore the role of heterogeneous biting and transmission on the establishment and spread of resistance in a population. We find that heterogeneous transmission slows the establishment of resistance in a population, but once resistance is established, it speeds the spread of resistance through the population. These results are due to the skewed distribution of biting which makes onward transmission a low probability and suggests that targeting the “core” group of individuals that provide the vast majority of bites could significantly slow the spread of resistance.

A Population Genetic Model for the Initial Spread of Partially Resistant Malaria Parasites under Anti-Malarial Combination Therapy and Weak Intrahost Competition

“…The relationship between λ and L T was still negative (for λ = 0.075, 0.1, 0.15, and 0.2, L T  = 34,076, 8,599, 1,134, and 625 generations, respectively), suggesting that the qualitative behavior of the system did not change. In addition, we considered a scenario in which transmission rate (frequency of mosquito bites) is higher for certain hosts than others [43]. Simulations were performed with host-to-mosquito transmission according to eq.…”

Cryptosporidium as a testbed for single cell genome characterization of unicellular eukaryotes

“…However, in case of infections with other parasites developing resistance to antiparasitic drugs, information on the population genetic setup in a specific infection could be important from a therapeutic perspective. The within-host interactions and treatment will strongly affect the evolution of resistance and multiple infections of malaria parasites have been predicted to spread drug resistance [ 44 , 45 ]. Knowing the resistance status of the population in the patient may guide medication and treatment strategies.…”

The Impact of Antimalarial Use on the Emergence and Transmission of Plasmodium falciparum Resistance: A Scoping Review of Mathematical Models

“…Of the 37 articles reviewed, nine modeled the emergence of resistance [ 8 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ], 19 modeled the transmission of resistance [ 6 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ] and nine modeled both [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. A summary of model elements and features is provided in Table 1 .…”

“…Mathematical modeling was identified to explore the dynamics of the emergence and transmission of antimalarial resistance taking into account antimalarial treatment use within the host population in nine articles [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. The emergence and transmission of resistance is predicted to increase with: An increase in the proportion of the population who receive antimalarial treatment (population coverage) [ 43 , 50 , 51 ]; Longer drug half-lives [ 43 ]; Residual drug concentrations [ 45 ]; An increased rate of parasite mutation [ 43 ]; An increased relative fitness of resistant P. falciparum parasites compared to their drug-sensitive counterparts [ 44 ]; A decrease in transmission intensity [ 47 ]; and The decreased use of transmission blockers (i.e., bednets) [ 49 ].…”