Superspreaders and residual malaria transmission in the main urban hotspot of Brazil

• Grant number: 23/15369-1
• Support Opportunities: Scholarships in Brazil - Doctorate
• Effective date (Start): March 01, 2024
• Effective date (End): September 30, 2027
• Field of knowledge: Health Sciences - Collective Health - Public Health
• Principal Investigator: Marcelo Urbano Ferreira
• Grantee: Winni Alves Ladeia
• Host Institution: Instituto de Ciências Biomédicas (ICB). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 22/11963-3 - Individual variation in malaria risk: causes and consequences in Amazonian populations, AP.TEM

Abstract

Nearly 130,000 cases of malaria are reported in Brazil each year, 99.9% of them in the Amazon Basin. Despite control measures, Mâncio Lima, the main urban hotspot of Brazil, has a high incidence and half of the cases are reportedly acquired in its urban area. We hypothesize that the residual malaria transmission can be sustained by a minority of high-risk people who are more likely to originate a high number of secondary infections. To examine the role of superspreading events in malaria transmission in Mâncio Lima, our first aim is to estimate key parameters in malaria transmission dynamics: (i) the effective reproduction number (Rt), which is the mean of the distribution of individual reproduction numbers (Ri = number of secondary infections originating from each index case) in the urban population of Mâncio Lima, and (ii) the dispersion parameter (k) of the negative binomial distribution, which describes the variation of Ri values in this population. To generate real-world estimates we will use AmpliSeq genotyping of two polymorphic markers per species. The identical parasite lineages will infer malaria transmission networks in this malaria suspect population assisted for 15 months. We anticipate genotyping approximately 1,800 samples. Our second aim is to identify correlates of increasing individual infectiousness, such as age, sex, parasite density, duration of infection, and place of residence. Characterizing transmission chains will allow malaria control programs to identify people who are more likely to be superspreaders and may be targeted by more intense preventive measures. To this end, we will use multivariable models to identify covariates that are significantly and independently associated with the number of secondary cases per infected individual.