RESUMO
In 2016 the World Health Organization identified 21 countries that could eliminate malaria by 2020. Monitoring progress towards this goal requires tracking ongoing transmission. Here we develop methods that estimate individual reproduction numbers and their variation through time and space. Individual reproduction numbers, Rc, describe the state of transmission at a point in time and differ from mean reproduction numbers, which are averages of the number of people infected by a typical case. We assess elimination progress in El Salvador using data for confirmed cases of malaria from 2010 to 2016. Our results demonstrate that whilst the average number of secondary malaria cases was below one (0.61, 95% CI 0.55-0.65), individual reproduction numbers often exceeded one. We estimate a decline in Rc between 2010 and 2016. However we also show that if importation is maintained at the same rate, the country may not achieve malaria elimination by 2020.
Assuntos
Malária/transmissão , Número Básico de Reprodução , El Salvador/epidemiologia , Doenças Endêmicas/prevenção & controle , Monitoramento Epidemiológico , Humanos , Incidência , Funções Verossimilhança , Malária/epidemiologia , Malária/prevenção & controle , Malária Falciparum/epidemiologia , Malária Falciparum/prevenção & controle , Malária Falciparum/transmissão , Malária Vivax/epidemiologia , Malária Vivax/prevenção & controle , Malária Vivax/transmissão , Fatores de Risco , Fatores de TempoRESUMO
Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state? In this paper, we model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Exploiting this connection, we develop a convex optimization framework for determining the required level of external drive in order for the network to reach a desired activity level. We experimented with event data gathered from Twitter, and show that our method can steer the activity of the network more accurately than alternatives.