Speaker
Description
Most available drugs kill malaria as it gets established in the liver or after it has infected red blood cells, but cannot tackle it once the parasite is released from the cells as gametocytes, which is when it is transmissible to other people via mosquito bites. Recently, promising clinical advances have been made in developing novel antimalarial drugs that block parasite transmission, cure the disease, and have prophylactic effects, called transmission-blocking drugs (TBDs) [2, 3, 4]. Our main aim is to explore the potential effects of such TBDs on malaria transmission in the effort to control and eliminate the disease using mathematical models to ascertain how the presence of TBDs can mitigate the transmission of malaria parasites on both asymptomatic and symptomatic carriers in a defined hotspot of malaria. Our special focus was on the effects of the treatment coverage and the efficacy of TBDs along with the protective effect and waning effect of TBDs. For this, we propose and analyze a mathematical model for malaria transmission dynamics that extends the SEIRS-SEI type model to include a class of humans undergoing treatment with TBDs and a class of those protected because of successful treatment. The mathematical and epidemiological implications of TBDs are assessed using different approaches.
Furthermore, we fit the model to malaria data using the library "lmfit” in Python and use the validated model to explore the model's predictions under various scenarios. Results from our analysis show that the effect of treatment coverage rate on reducing reproduction number depends on other key parameters such as the efficacy of the drug. The projections of the validated model show the benefits of using TBDs in malaria control in preventing new cases and reducing mortality [1]. We find that treating $35\%$ of the population of Sub-Saharan Africa with a $95\%$ efficacious TBD from $2021$ will result in approximately $82\%$ reduction on the number of malaria deaths by $2035$.
