Descrizione del progetto
Nuovi modelli matematici per eliminare la schistosomiasi
La schistosomiasi è una malattia tropicale endemica trascurata (NTD) causata da parassiti rilasciati da lumache d’acqua dolce. Classificandosi al secondo posto solo dopo la malaria come malattia parassitaria più diffusa, è la più letale di tutte le NTD. Il tasso di mortalità direttamente attribuibile alla schistosomiasi nell’Africa subsahariana è stimato a 280 000 all’anno, con milioni di persone che presentano sintomi clinici. L’Organizzazione mondiale della sanità (OMS) si è prefissata l’obiettivo di controllare la morbilità entro il 2020. Tuttavia, le infezioni ritornano rapidamente anche dopo una somministrazione massiccia di farmaci. Per capire perché questo accade, il progetto SchiSTOP svilupperà un modello stocastico basato su modello individuale (IBM) per simulare la trasmissione della schistosomiasi. A tal fine, nuovi modelli matematici e metodi statistici saranno integrati con i recenti progressi della genetica parassitaria, dell’epidemiologia, della diagnostica e dell’immunologia.
Obiettivo
Neglected tropical diseases (NTDs) are a diverse group of infections which are especially prevalent in low-income populations in tropical and subtropical areas in Africa, Asia and the Americas. Schistosomiasis is the deadliest NTD killing an estimated 280,000 people each year in the African region alone. The World Health Organization (WHO) 2020 target for schistosomiasis is to reduce the prevalence of heavy-intensity infections to ≤5% among school-aged children through Mass Drug Administration (MDA). However, in areas of moderate and high prevalence the goal seems unlikely to be met as even with intensive biannual MDA, prevalence of infections are still seen to rebound rapidly after each round. The reasons are still poorly understood. Mathematical models and statistical methods have proven to be essential to gain insights into the complex processes underlying the transmission dynamics of infectious diseases. In this research project, I will develop a stochastic individual-based model (IBM) to simulate schistosomiasis transmission integrating cutting-edge mathematical models and statistical methods with recent advances in parasite genetics, epidemiology, diagnostics and immunology. This project aims to advance our current understanding of schistosomiasis transmission dynamics investigating the mechanisms that cause the rapid rebound of prevalence after MDA and proposing optimal implementation of current and novel strategies to achieve schistosomiasis control and move towards elimination. In addition, this project will build a reference methodological framework to comprehensively study other MDA targeted infectious diseases. The proposed project addresses one of the priorities of the EU, through global poverty reduction (SDG goal 1) by promoting ways of improving future health (SDG goal 3).This project will be carried out at Erasmus MC with Prof. Sake J. de Vlas and at University of Glasgow (secondment) with Dr. Poppy Lamberton.
Campo scientifico
- medical and health scienceshealth sciencestropical medicine
- medical and health scienceshealth sciencespublic healthepidemiology
- medical and health scienceshealth sciencesinfectious diseases
- medical and health sciencesbasic medicineimmunology
- natural sciencesmathematicsapplied mathematicsmathematical model
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinatore
6525 GA Nijmegen
Paesi Bassi