Onchocerciasis-associated epilepsy in Maridi, South Sudan: Modelling and exploring the impact of control measures against river blindness.

BACKGROUND: Onchocerciasis, also known as "river blindness", is caused by the bite of infected female blackflies (genus Simuliidae) that transmit the parasite Onchocerca volvulus. A high onchocerciasis microfarial load increases the risk to develop epilepsy in children between the ages of 3 and 18 years. In resource-limited settings in Africa where onchocerciasis has been poorly controlled, high numbers of onchocerciasis-associated epilepsy (OAE) are reported. We use mathematical modeling to predict the impact of onchocerciasis control strategies on the incidence and prevalence of OAE. METHODOLOGY: We developed an OAE model within the well-established mathematical modelling framework ONCHOSIM. Using Latin-Hypercube Sampling (LHS), and grid search technique, we quantified transmission and disease parameters using OAE data from Maridi County, an onchocerciasis endemic area, in southern Republic of South Sudan. Using ONCHOSIM, we predicted the impact of ivermectin mass drug administration (MDA) and vector control on the epidemiology of OAE in Maridi. PRINCIPAL FINDINGS: The model estimated an OAE prevalence of 4.1% in Maridi County, close to the 3.7% OAE prevalence reported in field studies. The OAE incidence is expected to rapidly decrease by >50% within the first five years of implementing annual MDA with good coverage (≥70%). With vector control at a high efficacy level (around 80% reduction of blackfly biting rates) as the sole strategy, the reduction is slower, requiring about 10 years to halve the OAE incidence. Increasing the efficacy levels of vector control, and implementing vector control simultaneously with MDA, yielded better results in preventing new cases of OAE. CONCLUSIONS/SIGNIFICANCES: Our modeling study demonstrates that intensifying onchocerciasis eradication efforts could substantially reduce OAE incidence and prevalence in endemic foci. Our model may be useful for optimizing OAE control strategies.

The impact of rare but severe vaccine adverse events on behaviour-disease dynamics: a network model.

The propagation of rumours about rare but severe adverse vaccination or infection events through social networks can strongly impact vaccination uptake. Here we model a coupled behaviour-disease system where individual risk perception regarding vaccines and infection are shaped by their personal experiences and the experiences of others. Information about vaccines and infection either propagates through the network or becomes available through globally available sources. Dynamics are studied on a range of network types. Individuals choose to vaccinate according to their personal perception of risk and information about infection prevalence. We study events ranging from common and mild, to severe and rare. We find that vaccine and infection adverse events have asymmetric impacts. Vaccine (but not infection) adverse events may significantly prolong the tail of an outbreak. Similarly, introducing a small risk of a vaccine adverse event may cause a steep decline in vaccine coverage, especially on scale-free networks. Global dissemination of information about infection prevalence boosts vaccine coverage more than local dissemination. Taken together, these findings highlight the dangers associated with vaccine rumour propagation through scale-free networks such as those exhibited by online social media, as well as the benefits of disseminating public health information through mass media.

Modeling the impact of biolarvicides on malaria transmission.

Biolarvicides are in use in several parts of the world for malaria vector control. We propose a five compartment dynamical systems model to study malaria transmission when biolarvicides are administered, to study the impact of this environmentally safe method on malaria spread. A comprehensive analysis of the model is presented. Model analysis shows that the basic reproductive rate R is larger in the absence of biolarvicides as compared to their presence. Theoretical analysis is corroborated by data from field studies. We show that there exist intermediate parameter regimes that separate disease-free and endemic states, which can in turn be modulated by biolarvicide use. Using Latin hypercube sampling we study the sensitivity of the model to parameter value changes. Calibration of our model to mosquito population and biolarvicide data for indoor and outdoors scenarios, yield parameter values hitherto not available or measurable. We validate our model with malaria incidence data from a region in India and provide predictions for malaria incidence in the presence and absence of biolarvicide. This model provides a prognostic tool to field work involving biolarvicide use in control of malaria.