Substantiating freedom from parasitic infection by combining transmission model predictions with disease surveys

Abstract

Stopping interventions is a critical decision for parasite elimination programmes. Quantifying the probability that elimination has occurred due to interventions can be facilitated by combining infection status information from parasitological surveys with extinction thresholds predicted by parasite transmission models. Here we demonstrate how the integrated use of these two pieces of information derived from infection monitoring data can be used to develop an analytic framework for guiding the making of defensible decisions to stop interventions. We present a computational tool to perform these probability calculations and demonstrate its practical utility for supporting intervention cessation decisions by applying the framework to infection data from programmes aiming to eliminate onchocerciasis and lymphatic filariasis in Uganda and Nigeria, respectively. We highlight a possible method for validating the results in the field, and discuss further refinements and extensions required to deploy this predictive tool for guiding decision making by programme managers.

Base protocol for real time active random surveillance of coronavirus disease (COVID-19) – Adapting veterinary methodology to public health

“…Although the principles of the surveying protocol are derived from the animal health realm, where they have had extensive use [e.g. [4][5][6][7][8][9][10]], a similar approach in surveillance of the human population could be applicable [11], and requires public health leadership. At a first glance, the application of the proposed principles and protocol ( Figs.…”

“…In other words, the latter represents the cut-off at which the population would be considered as infected, if at least one positive is found out of those tested . The concept of "freedom" from disease has already had several applications in the veterinary field [4][5][6][7][8][9][10] and more recently, also in public health [11].…”

Data-driven modelling and spatial complexity supports heterogeneity-based integrative management for eliminating Simulium neavei-transmitted river blindness

“…While our BM-based modelling algorithm combines the advantageous features of mechanistic and statistical approaches to improve the estimation of local models for facilitating forecasts of interventions applied under a variety of field conditions, it is dependent, as for any data-driven predictive system, on the model structure employed, estimation procedure, and on the data used for facilitating model discovery 16,17 . Although our BM framework primarily focused attention on addressing parameter uncertainty with data, we note here firstly that the present model is based on previously established population models of onchocerciasis transmission 7,47 , with appropriate structural extensions made with regard to population-averaged mf uptake and larval development in the S. neavei vector host as well as the operation of different forms of host immunity in populations 48 . Furthermore, we have also secondly included all previously suggested density-dependent functions that are thought to govern onchocerciasis transmission but do not make any a priori assumptions concerning their occurrence using data, instead, to determine the operation of these functions in a site, which allows for a degree of updating for model structures applicable to a particular setting.…”

Assessment of control strategies against Clonorchis sinensis infection based on a multi-group dynamic transmission model

“…Values of many parameter could not be obtained from existing studies. Data-driven approaches, integrating survey data into models to estimate parameters that capture the local transmission dynamics [46], are most suitable approaches of parameter estimation for our study setting. Bayesian melding approach is one of the most common data-driven approaches [36,47,48].…”