Development of prediction models to identify hotspots of schistosomiasis in endemic regions to guide mass drug administration.

Schistosomiasis is a neglected tropical disease affecting over 150 million people. Hotspots of Schistosoma transmission-communities where infection prevalence does not decline adequately with mass drug administration-present a key challenge in eliminating schistosomiasis. Current approaches to identify hotspots require evaluation 2-5 y after a baseline survey and subsequent mass drug administration. Here, we develop statistical models to predict hotspots at baseline prior to treatment comparing three common hotspot definitions, using epidemiologic, survey-based, and remote sensing data. In a reanalysis of randomized trials in 589 communities in five endemic countries, a regression model predicts whether Schistosoma mansoni infection prevalence will exceed the WHO threshold of 10% in year 5 ("prevalence hotspot") with 86% sensitivity, 74% specificity, and 93% negative predictive value (NPV; assuming 30% hotspot prevalence), and a regression model for Schistosoma haematobium achieves 90% sensitivity, 90% specificity, and 96% NPV. A random forest model predicts whether S. mansoni moderate and heavy infection prevalence will exceed a public health goal of 1% in year 5 ("intensity hotspot") with 92% sensitivity, 79% specificity, and 96% NPV, and a boosted trees model for S. haematobium achieves 77% sensitivity, 95% specificity, and 91% NPV. Baseline prevalence is a top predictor in all models. Prediction is less accurate in countries not represented in training data and for a third hotspot definition based on relative prevalence reduction over time ("persistent hotspot"). These models may be a tool to prioritize high-risk communities for more frequent surveillance or intervention against schistosomiasis, but prediction of hotspots remains a challenge.

Infectiousness of SARS-CoV-2 breakthrough infections and reinfections during the Omicron wave.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) breakthrough infections in vaccinated individuals and reinfections in previously infected individuals have become increasingly common. Such infections highlight a broader need to understand the contribution of vaccination, including booster doses, and natural immunity to the infectiousness of individuals with SARS-CoV-2 infections, especially in high-risk populations with intense transmission, such as in prisons. Here we show that both vaccine-derived and naturally acquired immunity independently reduce the infectiousness of persons with Omicron variant SARS-CoV-2 infections in a prison setting. Analyzing SARS-CoV-2 surveillance data from December 2021 to May 2022 across 35 California state prisons with a predominately male population, we estimate that unvaccinated Omicron cases had a 36% (95% confidence interval (CI): 31-42%) risk of transmitting infection to close contacts, as compared to a 28% (25-31%) risk among vaccinated cases. In adjusted analyses, we estimated that any vaccination, prior infection alone and both vaccination and prior infection reduced an index case's risk of transmitting infection by 22% (6-36%), 23% (3-39%) and 40% (20-55%), respectively. Receipt of booster doses and more recent vaccination further reduced infectiousness among vaccinated cases. These findings suggest that, although vaccinated and/or previously infected individuals remain highly infectious upon SARS-CoV-2 infection in this prison setting, their infectiousness is reduced compared to individuals without any history of vaccination or infection. This study underscores benefit of vaccination to reduce, but not eliminate, transmission.

Infectiousness of SARS-CoV-2 breakthrough infections and reinfections during the Omicron wave.

SARS-CoV-2 breakthrough infections in vaccinated individuals and reinfections among previously infected individuals have become increasingly common. Such infections highlight a broader need to understand the contribution of vaccination, including booster doses, and natural immunity to the infectiousness of persons with SARS-CoV-2 infections, especially in high-risk populations with intense transmission such as prisons. Here, we show that both vaccine-derived and naturally acquired immunity independently reduce the infectiousness of persons with Omicron variant SARS-CoV-2 infections in a prison setting. Analyzing SARS-CoV-2 surveillance data from December 2021 to May 2022 across 35 California state prisons with a predominately male population, we estimate that unvaccinated Omicron cases had a 36% (95% confidence interval (CI): 31-42%) risk of transmitting infection to close contacts, as compared to 28% (25-31%) risk among vaccinated cases. In adjusted analyses, we estimated that any vaccination, prior infection alone, and both vaccination and prior infection reduced an index case's risk of transmitting infection by 22% (6-36%), 23% (3-39%) and 40% (20-55%), respectively. Receipt of booster doses and more recent vaccination further reduced infectiousness among vaccinated cases. These findings suggest that although vaccinated and/or previously infected individuals remain highly infectious upon SARS-CoV-2 infection in this prison setting, their infectiousness is reduced compared to individuals without any history of vaccination or infection, underscoring some benefit of vaccination to reduce but not eliminate transmission.

Evaluating strategies for spatial allocation of vaccines based on risk and centrality.

When vaccinating a large population in response to an invading pathogen, it is often necessary to prioritize some individuals to be vaccinated first. One way to do this is to choose individuals to vaccinate based on their location. Methods for this prioritization include strategies that target those regions most at risk of importing the pathogen, and strategies that target regions with high centrality on the travel network. We use a simple infectious disease epidemic model to compare a risk-targeting strategy to two different centrality-targeting strategies based on betweenness centrality and random walk percolation centrality, respectively. We find that the relative effectiveness of these strategies in reducing the total number of infections varies with the basic reproduction number of the pathogen, travel rates, structure of the travel network and vaccine availability. We conclude that when a pathogen has high spreading capacity, or when vaccine availability is limited, centrality-targeting strategies should be considered as an alternative to the more commonly used risk-targeting strategies.

The effect of the definition of 'pandemic' on quantitative assessments of infectious disease outbreak risk.

In the early stages of an outbreak, the term 'pandemic' can be used to communicate about infectious disease risk, particularly by those who wish to encourage a large-scale public health response. However, the term lacks a widely accepted quantitative definition. We show that, under alternate quantitative definitions of 'pandemic', an epidemiological metapopulation model produces different estimates of the probability of a pandemic. Critically, we show that using different definitions alters the projected effects of key parameters-such as inter-regional travel rates, degree of pre-existing immunity, and heterogeneity in transmission rates between regions-on the risk of a pandemic. Our analysis provides a foundation for understanding the scientific importance of precise language when discussing pandemic risk, illustrating how alternative definitions affect the conclusions of modelling studies. This serves to highlight that those working on pandemic preparedness must remain alert to the variability in the use of the term 'pandemic', and provide specific quantitative definitions when undertaking one of the types of analysis that we show to be sensitive to the pandemic definition.