Quantitative Risk Assessment for Bovine Spongiform Encephalopathy in Low- or Zero-Prevalence Countries: The Example of Norway

A predictive case-cohort model is applied to Norwegian data to analyze the interaction between challenge and stability factors for bovine spongiform encephalopathy (BSE) during the period 1980-2010. For each year, the BSE risk in cattle is estimated as the expected number of cases. The age distribution of expected cases as well as the relative impact of different challenges is estimated. The model consists of a simple, transparent, and practical deterministic spreadsheet calculation model, in which the following country-specific inputs are entered: (i) annual imports of live cattle and meat and bone meal, (ii) age distribution of native cattle, and (iii) estimated annual basic reproduction ratio (R(0)) for BSE. Results for Norway indicate that the highest risk of BSE cases was in 1989, when a total BSE risk of 0.13 cases per year was expected. After that date, the year-to-year decrease in risk ranged between 3% and 47%, except for a secondary peak in 1994 at 0.06 cases per year. The primary peak was almost entirely (99%) attributable to the importation of 11 cattle from the United Kingdom between 1982 and 1986. The secondary peak, in 1994, originated mainly from the recycling of the U.K. imported cattle (92%). In 2006, the remaining risk was 0.0003 cases per year, or 0.001 per million cows per year, with a maximal age-specific incidence of 0.03 cases per million per year in 10-year-old cattle. Only 15% of the cases were expected in imported cattle. The probability of having zero cases in Norway in 2006 was estimated to be 99.97%. The model and results are compared to previous risk assessments of Norway by the EU.     

Development and Consideration of Global Policies for Managing the Future Risks of Poliovirus Outbreaks: Insights and Lessons Learned Through Modeling

The success of the Global Polio Eradication Initiative promises to bring large benefits, including sustained improvements in quality of life (i.e., cases of paralytic disease and deaths avoided) and costs saved from cessation of vaccination. Obtaining and maintaining these benefits requires that policymakers manage the transition from the current massive use of oral poliovirus vaccine (OPV) to a world without OPV and free of the risks of potential future reintroductions of live polioviruses. This article describes the analytical journey that began in 2001 with a retrospective case study on polio risk management and led to development of dynamic integrated risk, economic, and decision analysis tools to inform global policies for managing the risks of polio. This analytical journey has provided several key insights and lessons learned that will be useful to future analysts involved in similar complex decision-making processes.     

Discovering Spatio-Temporal Models of the Spread of West Nile Virus

Emerging infectious diseases are characterized by complex interactions among disease agents, vectors, wildlife, humans, and the environment. Since the appearance of West Nile virus (WNV) in New York City in 1999, it has infected over 8,000 people in the United States, resulting in several hundred deaths in 46 contiguous states. The virus is transmitted by mosquitoes and maintained in various bird reservoir hosts. Its unexpected introduction, high morbidity, and rapid spread have left public health agencies facing severe time constraints in a theory-poor environment, dependent largely on observational data collected by independent survey efforts and much uncertainty. Current knowledge may be expressed as a priori constraints on models learned from data. Accordingly, we applied a Bayesian probabilistic relational approach to generate spatially and temporally linked models from heterogeneous data sources. Using data collected from multiple independent sources in Maryland, we discovered the integrated context in which infected birds are plausible indicators for positive mosquito pools and human cases for 2001 and 2002.     

Using repeated-prevalence data in multi-state modeling of renal replacement therapy

Multi-state models help predict future numbers of patients requiring specific treatments but these models require exhaustive incidence data. Deriving reliable predictions from repeated-prevalence data would be helpful. A new method to model the number of patients that switch between therapeutic modalities using repeated-prevalence data is presented and illustrated. The parameters and goodness of fit obtained with the new method and repeated-prevalence data were compared to those obtained with the classical method and incidence data. The multi-state model parameters’ confidence intervals obtained with annually collected repeated-prevalence data were wider than those obtained with incidence data and six out of nine pairs of confidence intervals did not overlap. However, most parameters were of the same order of magnitude and the predicted patient distributions among various renal replacement therapies were similar regardless of the type of data used. In the absence of incidence data, a multi-state model can still be successfully built with annually collected repeated-prevalence data to predict the numbers of patients requiring specific treatments. This modeling technique can be extended to other chronic diseases.     

Overcoming the ‘tyranny of the urgent’: integrating gender into disease outbreak preparedness and response

ABSTRACT

This article contributes to discussions on the gender dimensions of disease outbreaks, and preparedness policies and responses, by providing a multi-level analysis of gender-related gaps, particularly illustrating how the failure to challenge gender assumptions and incorporate gender as a priority at the global level has national and local impacts. The implications of neglecting gender dynamics, as well as the potential of equity-based approaches to disease outbreak responses, is illustrated through a case study of the Social Enterprise Network for Development (SEND) Sierra Leone, a non-government organisation (NGO) based in Kailahun, during the Ebola outbreak.