Estimating infectious disease transmission distances using the overall distribution of cases

The average spatial distance between transmission-linked cases is a fundamental property of infectious disease dispersal. However, the distance between a case and their infector is rarely measurable. Contact-tracing investigations are resource intensive or even impossible, particularly when only a subset of cases are detected. Here, we developed an approach that uses onset dates, the generation time distribution and location information to estimate the mean transmission distance. We tested our method using outbreak simulations. We then applied it to the 2001 foot-and-mouth outbreak in Cumbria, UK, and compared our results to contact-tracing activities. In simulations with a true mean distance of 106 m, the average mean distance estimated was 109 m when cases were fully observed (95% range of 71–142). Estimates remained consistent with the true mean distance when only five percent of cases were observed, (average estimate of 128 m, 95% range 87–165). Estimates were robust to spatial heterogeneity in the underlying population. We estimated that both the mean and the standard deviation of the transmission distance during the 2001 foot-and-mouth outbreak was 8.9 km (95% CI: 8.4 km–9.7 km). Contact-tracing activities found similar values of 6.3 km (5.2km–7.4 km) and 11.2 km (9.5 km–12.8 km), respectively. We were also able to capture the drop in mean transmission distance over the course of the outbreak. Our approach is applicable across diseases, robust to under-reporting and can inform interventions and surveillance

Maintaining high rates of measles immunization in Africa

Supplementary immunization activities (SIAs) are important in achieving high levels of population immunity to measles virus. Using data from a 2006 survey of measles vaccination in Lusaka, Zambia, we developed a model to predict measles immunity following routine vaccination and SIAs, and absent natural infection. Projected population immunity was compared between the current programme and alternatives, including supplementing routine vaccination with a second dose, or SIAs at 1-, 2-, 3-, 4- and 5-year intervals. Current routine vaccination plus frequent SIAs could maintain high levels of population immunity in children aged <5 years, even if each frequent SIA has low coverage (e.g. ≥72% for bi-annual 60% coverage SIAs vs. ≥69% for quadrennial 95% coverage SIAs). A second dose at 12 months with current coverage could achieve 81% immunity. Circulating measles virus will only increase population immunity. Public health officials should consider frequent SIAs when resources for a two-dose strategy are unavailable

Review article: Incubation periods of mosquito-borne viral infections: a systematic review

Mosquito-borne viruses are a major public health threat, but their incubation periods are typically uncited, non-specific, and not based on data. We systematically review the published literature on six mosquito-borne viruses selected for their public health importance: chikungunya, dengue, Japanese encephalitis, Rift Valley fever, West Nile, and yellow fever viruses. For each, we identify the literature's consensus on the incubation period, evaluate the evidence for this consensus, and provide detailed estimates of the incubation period and distribution based on published experimental and observational data. We abstract original data as doubly interval-censored observations. Assuming a log-normal distribution, we estimate the median incubation period, dispersion, 25th and 75th percentiles by maximum likelihood. We include bootstrapped 95% confidence intervals for each estimate. For West Nile and yellow fever viruses, we also estimate the 5th and 95th percentiles of their incubation periods

Case Study in Evaluating Time Series Prediction Models Using the Relative Mean Absolute Error

Statistical prediction models inform decision-making processes in many real-world settings. Prior to using predictions in practice, one must rigorously test and validate candidate models to ensure that the proposed predictions have sufficient accuracy to be used in practice. In this article, we present a framework for evaluating time series predictions, which emphasizes computational simplicity and an intuitive interpretation using the relative mean absolute error metric. For a single time series, this metric enables comparisons of candidate model predictions against naïve reference models, a method that can provide useful and standardized performance benchmarks. Additionally, in applications with multiple time series, this framework facilitates comparisons of one or more models’ predictive performance across different sets of data. We illustrate the use of this metric with a case study comparing predictions of dengue hemorrhagic fever incidence in two provinces of Thailand. This example demonstrates the utility and interpretability of the relative mean absolute error metric in practice, and underscores the practical advantages of using relative performance metrics when evaluating predictions

Differential mobility and local variation in infection attack rate

Infectious disease transmission in animals is an inherently spatial process in which a host's home location and their social mixing patterns are important, with the mixing of infectious individuals often different to that of susceptible individuals. Although incidence data for humans have traditionally been aggregated into low-resolution data sets, modern representative surveillance systems such as electronic hospital records generate high volume case data with precise home locations. Here, we use a gridded spatial transmission model of arbitrary resolution to investigate the theoretical relationship between population density, differential population movement and local variability in incidence. We show analytically that a uniform local attack rate is only possible for individual pixels in the grid if susceptible and infectious individuals move in the same way. Using a population in Guangdong, China, for which a robust quantitative description of movement is available (a movement kernel), and a natural history consistent with pandemic influenza; we show that local cumulative incidence is positively correlated with population density when susceptible individuals are more connected in space than infectious individuals. Conversely, under the less intuitively likely scenario, when infectious individuals are more connected, local cumulative incidence is negatively correlated with population density. The strength and direction of correlation changes sign for other kernel parameter values. We show that simulation models in which it is assumed implicitly that only infectious individuals move are assuming a slightly unusual specific correlation between population density and attack rate. However, we also show that this potential structural bias can be corrected by using the appropriate non-isotropic kernel that maps infectious-only code onto the isotropic dual-mobility kernel. These results describe a precise relationship between the spatio-social mixing of infectious and susceptible individuals and local variability in attack rates. More generally, these results suggest a genuine risk that mechanistic models of high-resolution attack rate data may reach spurious conclusions if the precise implications of spatial force-of-infection assumptions are not first fully characterized, prior to models being fit to data

An open source tool to infer epidemiological and immunological dynamics from serological data: Serosolver

We present a flexible, open source R package designed to obtain biological and epidemiological insights from serological datasets. Characterising past exposures for multi-strain pathogens poses a specific statistical challenge: observed antibody responses measured in serological assays depend on multiple unobserved prior infections that produce cross-reactive antibody responses. We provide a general modelling framework to jointly infer infection histories and describe immune responses generated by these infections using antibody titres against current and historical strains. We do this by linking latent infection dynamics with a mechanistic model of antibody kinetics that generates expected antibody titres over time. Our aim is to provide a flexible package to identify infection histories that can be applied to a range of pathogens. We present two case studies to illustrate how our model can infer key immunological parameters, such as antibody titre boosting, waning and crossreaction, as well as latent epidemiological processes such as attack rates and age-stratified infection risk

Times to key events in Zika virus infection and implications for blood donation: A systematic review

Objective To estimate the timing of key events in the natural history of Zika virus infection. Methods In February 2016, we searched PubMed, Scopus and the Web of Science for publications containing the term Zika. By pooling data, we estimated the incubation period, the time to seroconversion and the duration of viral shedding. We estimated the risk of Zika virus contaminated blood donations. Findings We identified 20 articles on 25 patients with Zika virus infection. The median incubation period for the infection was estimated to be 5.9 days (95% credible interval, CrI: 4.4-7.6), with 95% of people who developed symptoms doing so within 11.2 days (95% CrI: 7.6-18.0) after infection. On average, seroconversion occurred 9.1 days (95% CrI: 7.0-11.6) after infection. The virus was detectable in blood for 9.9 days (95% CrI: 6.9-21.4) on average. Without screening, the estimated risk that a blood donation would come from an infected individual increased by approximately 1 in 10 000 for every 1 per 100 000 person-days increase in the incidence of Zika virus infection. Symptom-based screening may reduce this rate by 7% (relative risk, RR: 0.93; 95% CrI: 0.89-0.99) and antibody screening, by 29% (RR: 0.71; 95% CrI: 0.28-0.88). Conclusion Neither symptom- nor antibody-based screening for Zika virus infection substantially reduced the risk that blood donations would be contaminated by the virus. Polymerase chain reaction testing should be considered for identifying blood safe for use in pregnant women in high-incidence areas

Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S.

Reconstructing the incidence of SARS-CoV-2 infection is central to understanding the state of the pandemic. Seroprevalence studies are often used to assess cumulative infections as they can identify asymptomatic infection. Since July 2020, commercial laboratories have conducted nationwide serosurveys for the U.S. CDC. They employed three assays, with different sensitivities and specificities, potentially introducing biases in seroprevalence estimates. Using models, we show that accounting for assays explains some of the observed state-to-state variation in seroprevalence, and when integrating case and death surveillance data, we show that when using the Abbott assay, estimates of proportions infected can differ substantially from seroprevalence estimates. We also found that states with higher proportions infected (before or after vaccination) had lower vaccination coverages, a pattern corroborated using a separate dataset. Finally, to understand vaccination rates relative to the increase in cases, we estimated the proportions of the population that received a vaccine prior to infection

Forty years of dengue surveillance at a tertiary pediatric hospital in Bangkok, Thailand, 1973-2012

Long-term observational studies can provide valuable insights into overall dengue epidemiology. Here, we present analysis of dengue cases at a pediatric hospital in Bangkok, Thailand, during a 40-year period from 1973 to 2012. Data were analyzed from 25,715 hospitalized patients with laboratory-confirmed dengue virus (DENV) infection. Several long-term trends in dengue disease were identified including an increase in mean age of hospitalized cases from an average of 7-8 years, an increase after 1990 in the proportion of post-primary cases for DENV-1 and DENV-3, and a decrease in the proportion of dengue hemorrhagic fever and dengue shock syndrome cases in primary and post-primary cases over time. Exploratory mechanistic analysis of these observed trends considered changes in diagnostic methods, demography, force of infection, and Japanese encephalitis vaccination as possible explanations. Thailand is an important setting for studying DENV transmission as it has a "mature" dengue epidemiology with a strong surveillance system in place since the early 1970s. We characterized changes in dengue epidemiology over four decades, and possible impact of demographic and other changes in the human population. These results may inform other countries where similar changes in transmission and population demographics may now or may soon be occurring

Impacts of Zika emergence in Latin America on endemic dengue transmission

In 2015 and 2016, Zika virus (ZIKV) swept through dengue virus (DENV) endemic areas of Latin America. These viruses are of the same family, share a vector and may interact competitively or synergistically through human immune responses. We examine dengue incidence from Brazil and Colombia before, during, and after the Zika epidemic. We find evidence that dengue incidence was atypically low in 2017 in both countries. We investigate whether subnational Zika incidence is associated with changes in dengue incidence and find mixed results. Using simulations with multiple assumptions of interactions between DENV and ZIKV, we find cross-protection suppresses incidence of dengue following Zika outbreaks and low periods of dengue incidence are followed by resurgence. Our simulations suggest correlations in DENV and ZIKV reproduction numbers could complicate associations between ZIKV incidence and post-ZIKV DENV incidence and that periods of low dengue incidence are followed by large increases in dengue incidence