Six challenges in modelling for public health policy.

The World Health Organisation's definition of public health refers to all organized measures to prevent disease, promote health, and prolong life among the population as a whole (World Health Organization, 2014). Mathematical modelling plays an increasingly important role in helping to guide the most high impact and cost-effective means of achieving these goals. Public health programmes are usually implemented over a long period of time with broad benefits to many in the community. Clinical trials are seldom large enough to capture these effects. Observational data may be used to evaluate a programme after it is underway, but have limited value in helping to predict the future impact of a proposed policy. Furthermore, public health practitioners are often required to respond to new threats, for which there is little or no previous data on which to assess the threat. Computational and mathematical models can help to assess potential threats and impacts early in the process, and later aid in interpreting data from complex and multifactorial systems. As such, these models can be critical tools in guiding public health action. However, there are a number of challenges in achieving a successful interface between modelling and public health. Here, we discuss some of these challenges

Controlled Phenylhydrazine-Induced Reticulocytosis in the Rat

Author Institution: Department of Physiology, Ohio State University, College of Medicine, Columbus, Ohio 43210The pattern of development of phenylhydrazine-induced rat reticulocytosis was studied over a period of nine days. Intraperitoneal injections of phenylhydrazine (4 mg/100 gm) every other day caused a fall in hematocrit which leveled off at 60% of normal by the fifth day. Increased erythropoiesis was indicated by a three-fold increase in the number of circulating reituclocytes after the first three injections. The immediate response was the release of stored mature reticulocytes from the bone marrow. As the anemia progressed, more and more young reticulocytes appeared until 70 to 85% of the red cells in the peripheral circulation were reticulocytes and 20% of these were juvenile forms

Urban Cholera transmission hotspots and their implications for Reactive Vaccination: evidence from Bissau city, Guinea Bissau

Use of cholera vaccines in response to epidemics (reactive vaccination) may provide an effective supplement to traditional control measures. In Haiti, reactive vaccination was considered but, until recently, rejected in part due to limited global supply of vaccine. Using Bissau City, Guinea-Bissau as a case study, we explore neighborhood-level transmission dynamics to understand if, with limited vaccine and likely delays, reactive vaccination can significantly change the course of a cholera epidemic

Seven challenges for model-driven data collection in experimental and observational studies

Infectious disease models are both concise statements of hypotheses and powerful techniques for creating tools from hypotheses and theories. As such, they have tremendous potential for guiding data collection in experimental and observational studies, leading to more efficient testing of hypotheses and more robust study designs. In numerous instances, infectious disease models have played a key role in informing data collection, including the Garki project studying malaria, the response to the 2009 pandemic of H1N1 influenza in the United Kingdom and studies of T-cell immunodynamics in mammals. However, such synergies remain the exception rather than the rule; and a close marriage of dynamic modeling and empirical data collection is far from the norm in infectious disease research. Overcoming the challenges to using models to inform data collection has the potential to accelerate innovation and to improve practice in how we deal with infectious disease threats

What Now? Epidemiology in the Wake of a Pandemic

The coronavirus disease 2019 (COVID-19) pandemic and the coming transition to a postpandemic world where COVID-19 will likely remain as an endemic disease present a host of challenges and opportunities in epidemiologic research. The scale and universality of this disruption to life and health provide unique opportunities to study phenomena and health challenges in all branches of epidemiology, from the obvious infectious disease and social consequences to less clear impacts on chronic disease and cancer. If we are to both take advantage of the largest natural experiment of our lifetimes and provide evidence to inform the numerous public health and clinical decisions being made every day, we must act quickly to ask critical questions and develop new methods for answering them. In doing so, we should build on each of our strengths and expertise and try to provide new insights rather than become yet another voice commenting on the same set of questions with limited evidence

Seven challenges for model-driven data collection in experimental and observational studies.

Infectious disease models are both concise statements of hypotheses and powerful techniques for creating tools from hypotheses and theories. As such, they have tremendous potential for guiding data collection in experimental and observational studies, leading to more efficient testing of hypotheses and more robust study designs. In numerous instances, infectious disease models have played a key role in informing data collection, including the Garki project studying malaria, the response to the 2009 pandemic of H1N1 influenza in the United Kingdom and studies of T-cell immunodynamics in mammals. However, such synergies remain the exception rather than the rule; and a close marriage of dynamic modeling and empirical data collection is far from the norm in infectious disease research. Overcoming the challenges to using models to inform data collection has the potential to accelerate innovation and to improve practice in how we deal with infectious disease threats

Differential mobility and local variation in infection attack rate.

Infectious disease transmission 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 typically 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 travel 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

Measuring the Performance of Vaccination Programs Using Cross-Sectional Surveys: A Likelihood Framework and Retrospective Analysis

Justin Lessler and colleagues describe a method that estimates the fraction of a population accessible to vaccination activities, and they apply it to measles vaccination in three African countries: Ghana, Madagascar, and Sierra Leone

High Hepatitis E Seroprevalence Among Displaced Persons in South Sudan.

AbstractLarge protracted outbreaks of hepatitis E virus (HEV) have been documented in displaced populations in Africa over the past decade though data are limited outside these exceptional settings. Serological studies can provide insights useful for improving surveillance and disease control. We conducted an age-stratified serological survey using samples previously collected for another research study from 206 residents of an internally displaced person camp in Juba, South Sudan. We tested serum for anti-HEV antibodies (IgM and IgG) and estimated the prevalence of recent and historical exposure to the virus. Using data on individuals' serostatus, camp arrival date, and state of origin, we used catalytic transmission models to estimate the relative risk of HEV infection in the camp compared with that in the participants' home states. The age-adjusted seroprevalence of anti-HEV IgG was 71% (95% confidence interval = 63-78), and 4% had evidence of recent exposure (IgM). We estimated HEV exposure rates to be more than 2-fold (hazard ratio = 2.3, 95% credible interval = 0.3-5.8) higher in the camp than in the participants' home states, although this difference was not statistically significant. HEV transmission may be higher than previously appreciated, even in the absence of reported cases. Improved surveillance in similar settings is needed to understand the burden of disease and minimize epidemic impact through early detection and response

Evidence for antigenic seniority in influenza A (H3N2) antibody responses in southern China

A key observation about the human immune response to repeated exposure to influenza A is that the first strain infecting an individual apparently produces the strongest adaptive immune response. Although antibody titers measure that response, the interpretation of titers to multiple strains - from the same sera - in terms of infection history is clouded by age effects, cross reactivity and immune waning. From July to September 2009, we collected serum samples from 151 residents of Guangdong Province, China, 7 to 81 years of age. Neutralization tests were performed against strains representing six antigenic clusters of H3N2 influenza circulating between 1968 and 2008, and three recent locally circulating strains. Patterns of neutralization titers were compared based on age at time of testing and age at time of the first isolation of each virus. Neutralization titers were highest for H3N2 strains that circulated in an individual's first decade of life (peaking at 7 years). Further, across strains and ages at testing, statistical models strongly supported a pattern of titers declining smoothly with age at the time a strain was first isolated. Those born 10 or more years after a strain emerged generally had undetectable neutralization titers to that strain (<1:10). Among those over 60 at time of testing, titers tended to increase with age. The observed pattern in H3N2 neutralization titers can be characterized as one of antigenic seniority: repeated exposure and the immune response combine to produce antibody titers that are higher to more 'senior' strains encountered earlier in life. © 2012 Lessler et al.published_or_final_versio