Practical considerations for measuring the effective reproductive number, Rt.

Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation

Who gets infected and why: Confronting models with data to determine drivers of pathogen susceptibility at the individual and population-level

Host susceptibility is a foundational concept in infectious disease dynamics. Susceptible individuals are the fuel that allows outbreaks to grow and spread. Once an epidemic takes hold, depletion of susceptible hosts (through new infections) eventually drives the effective reproduction number (Reff) below 1, causing the outbreak to stutter and fade. Eventually, the demographic buildup of new susceptible hosts (via new births) creates conditions hospitable to a new epidemic cycle. Ultimately, the fraction of the population susceptible to a given pathogen, and heterogeneity in individual susceptibility by age, by birth year or by physiological status, determine whether a pathogen can spread and persist in a given host population. Despite its crucial importance, understanding how host susceptibility is distributed across populations is a perennial challenge. Many pathogens of humans and animals have complex strain structure, with partial cross-protection acting among a variety of serotypes. Immunity to other pathogens may wane over time, or may reduce disease severity without entirely preventing infection. For the myriad pathogens with these characteristics, host susceptibility can be difficult to model and difficult to measure, even when serological data on antibody titers is available. Individual susceptibility is an emergent property of within-host interactions between pathogens and immune effectors. The specific immune interactions that determine susceptibility are often pathogen-specific, and difficult to observe. However, individual-level data on infection outcomes, or population-level epidemiological data are abundant. Statistical analysis of these existing data can help identify host-level factors that govern individual susceptibility. In turn these insights can be used to improve our understanding of how susceptibility is distributed across the population, and predictions of epidemic spread. These inferences can also provide clues to the underlying molecular drivers of host immunity against specific pathogens. In chapter 1, I compile publicly available data on two avian influenza viruses, H5N1 and H7N9, which have each spilled over to cause hundreds of human cases. I perform likelihood-based model comparison on these data to show that individuals gain exceptionally strong, lifelong protection against avian influenza subtypes in the same phylogenetic group as the first influenza virus encountered in childhood. These results show that susceptibility varies systematically with birth year, and challenge the long-standing assumption that antigenically novel, zoonotic or pandemic influenza viruses escape pre-existing immunity when they spill over to cause cases in humans. These results can help explain why certain birth years have been spared during past influenza pandemics, and may help improve birth year-specific forecasts of future pandemic risk. Further, these results suggest an antigenic basis for naturally-occurring, broadly protective influenza immunity.In chapter 2, I analyze a large epidemiological surveillance data set to ask whether the same patterns of broadly protective childhood immune imprinting shape birth year-specific risk from the seasonal influenza viruses that cause wintertime epidemics in humans. Model selection shows that seasonal influenza risk from subtypes H1N1 and H3N2 is indeed tied to birth year, and shaped by childhood immune imprinting. However, unlike for avian influenza, immune cross-protection acts more narrowly. Individuals only gain imprinting protection against seasonal influenza viruses of the very same antigenic subtype as the first virus encountered in childhood. Together, results from chapters 1 and 2 provide a partial proof of concept for development of universal influenza vaccines. Chapter 1 illustrates that the sort of broadly protective immune responses that universal vaccines would aim to elicit can already act naturally in human populations, and in certain epidemic contexts, already seem to shape population susceptibility. But chapter 2 highlights the difficulty of deploying these broadly protective immune responses against familiar, high-burden seasonal strains. Taken alongside recent immunological evidence, these results suggest that the breadth of immune cross-protection against influenza viruses is not fixed, but instead is an emergent property of within-host competition between B cell (antibody-producing) clones. On exposure to a familiar, seasonal influenza virus, narrowly-protective B cell clones competitively exclude broadly protective clones, and the antibody response provides only narrow immune cross-protection, against a single viral subtype. But on exposure to a novel, avian influenza virus, the host may only recognize conserved viral epitopes, and more broadly protective B cell clones are transiently released from competition.In chapter 3, I shift my focus from childhood imprinting history to explore another dimension of host susceptibility, the role of physical immune barriers in infection resistance. I develop a mechanistic dose-response model to identify factors that limit the spillover of an environmentally abundant bacterial pathogen, Leptospira interrogans. Hosts living in contaminated environments may be exposed to low doses of Leptospira on a daily basis, yet not all become infected. Using data from animal challenge experiments, we show that broken skin is most likely necessary for low-dose environmental exposures to cause infection. Together, these studies illustrate that heterogeneity in host susceptibility can be linked to measurable, underlying drivers. Demographic factors like year of birth, and immune history predictably modulate susceptibility to specific influenza virus subtypes. Physiological factors, like the presence of wounds and abrasions, predictably modulate susceptibility to environmentally persistent bacterial pathogens like Leptospira. By developing models based on biological principles and then confronting those models with data, we can identify specific risk factors that govern individual susceptibility against specific pathogens. Scaling these insights up to the population level can improve our ability to estimate key epidemiological parameters and can help guide the distribution of limited treatment or prevention resources during outbreaks

Who gets infected and why: Confronting models with data to determine drivers of pathogen susceptibility at the individual and population-level

Host susceptibility is a foundational concept in infectious disease dynamics. Susceptible individuals are the fuel that allows outbreaks to grow and spread. Once an epidemic takes hold, depletion of susceptible hosts (through new infections) eventually drives the effective reproduction number (Reff) below 1, causing the outbreak to stutter and fade. Eventually, the demographic buildup of new susceptible hosts (via new births) creates conditions hospitable to a new epidemic cycle. Ultimately, the fraction of the population susceptible to a given pathogen, and heterogeneity in individual susceptibility by age, by birth year or by physiological status, determine whether a pathogen can spread and persist in a given host population. Despite its crucial importance, understanding how host susceptibility is distributed across populations is a perennial challenge. Many pathogens of humans and animals have complex strain structure, with partial cross-protection acting among a variety of serotypes. Immunity to other pathogens may wane over time, or may reduce disease severity without entirely preventing infection. For the myriad pathogens with these characteristics, host susceptibility can be difficult to model and difficult to measure, even when serological data on antibody titers is available. Individual susceptibility is an emergent property of within-host interactions between pathogens and immune effectors. The specific immune interactions that determine susceptibility are often pathogen-specific, and difficult to observe. However, individual-level data on infection outcomes, or population-level epidemiological data are abundant. Statistical analysis of these existing data can help identify host-level factors that govern individual susceptibility. In turn these insights can be used to improve our understanding of how susceptibility is distributed across the population, and predictions of epidemic spread. These inferences can also provide clues to the underlying molecular drivers of host immunity against specific pathogens. In chapter 1, I compile publicly available data on two avian influenza viruses, H5N1 and H7N9, which have each spilled over to cause hundreds of human cases. I perform likelihood-based model comparison on these data to show that individuals gain exceptionally strong, lifelong protection against avian influenza subtypes in the same phylogenetic group as the first influenza virus encountered in childhood. These results show that susceptibility varies systematically with birth year, and challenge the long-standing assumption that antigenically novel, zoonotic or pandemic influenza viruses escape pre-existing immunity when they spill over to cause cases in humans. These results can help explain why certain birth years have been spared during past influenza pandemics, and may help improve birth year-specific forecasts of future pandemic risk. Further, these results suggest an antigenic basis for naturally-occurring, broadly protective influenza immunity.In chapter 2, I analyze a large epidemiological surveillance data set to ask whether the same patterns of broadly protective childhood immune imprinting shape birth year-specific risk from the seasonal influenza viruses that cause wintertime epidemics in humans. Model selection shows that seasonal influenza risk from subtypes H1N1 and H3N2 is indeed tied to birth year, and shaped by childhood immune imprinting. However, unlike for avian influenza, immune cross-protection acts more narrowly. Individuals only gain imprinting protection against seasonal influenza viruses of the very same antigenic subtype as the first virus encountered in childhood. Together, results from chapters 1 and 2 provide a partial proof of concept for development of universal influenza vaccines. Chapter 1 illustrates that the sort of broadly protective immune responses that universal vaccines would aim to elicit can already act naturally in human populations, and in certain epidemic contexts, already seem to shape population susceptibility. But chapter 2 highlights the difficulty of deploying these broadly protective immune responses against familiar, high-burden seasonal strains. Taken alongside recent immunological evidence, these results suggest that the breadth of immune cross-protection against influenza viruses is not fixed, but instead is an emergent property of within-host competition between B cell (antibody-producing) clones. On exposure to a familiar, seasonal influenza virus, narrowly-protective B cell clones competitively exclude broadly protective clones, and the antibody response provides only narrow immune cross-protection, against a single viral subtype. But on exposure to a novel, avian influenza virus, the host may only recognize conserved viral epitopes, and more broadly protective B cell clones are transiently released from competition.In chapter 3, I shift my focus from childhood imprinting history to explore another dimension of host susceptibility, the role of physical immune barriers in infection resistance. I develop a mechanistic dose-response model to identify factors that limit the spillover of an environmentally abundant bacterial pathogen, Leptospira interrogans. Hosts living in contaminated environments may be exposed to low doses of Leptospira on a daily basis, yet not all become infected. Using data from animal challenge experiments, we show that broken skin is most likely necessary for low-dose environmental exposures to cause infection. Together, these studies illustrate that heterogeneity in host susceptibility can be linked to measurable, underlying drivers. Demographic factors like year of birth, and immune history predictably modulate susceptibility to specific influenza virus subtypes. Physiological factors, like the presence of wounds and abrasions, predictably modulate susceptibility to environmentally persistent bacterial pathogens like Leptospira. By developing models based on biological principles and then confronting those models with data, we can identify specific risk factors that govern individual susceptibility against specific pathogens. Scaling these insights up to the population level can improve our ability to estimate key epidemiological parameters and can help guide the distribution of limited treatment or prevention resources during outbreaks

Effectiveness of traveller screening for emerging pathogens is shaped by epidemiology and natural history of infection.

During outbreaks of high-consequence pathogens, airport screening programs have been deployed to curtail geographic spread of infection. The effectiveness of screening depends on several factors, including pathogen natural history and epidemiology, human behavior, and characteristics of the source epidemic. We developed a mathematical model to understand how these factors combine to influence screening outcomes. We analyzed screening programs for six emerging pathogens in the early and late stages of an epidemic. We show that the effectiveness of different screening tools depends strongly on pathogen natural history and epidemiological features, as well as human factors in implementation and compliance. For pathogens with longer incubation periods, exposure risk detection dominates in growing epidemics, while fever becomes a better target in stable or declining epidemics. For pathogens with short incubation, fever screening drives detection in any epidemic stage. However, even in the most optimistic scenario arrival screening will miss the majority of cases

Potent protection against H5N1 and H7N9 influenza via childhood hemagglutinin imprinting

Two zoonotic influenza A viruses (IAV) of global concern, H5N1 and H7N9, exhibit unexplained differences in age distribution of human cases. Using data from all known human cases of these viruses, we show that an individual's first IAV infection confers lifelong protection against severe disease from novel hemagglutinin (HA) subtypes in the same phylogenetic group. Statistical modeling shows that protective HA imprinting is the crucial explanatory factor, and it provides 75% protection against severe infection and 80% protection against death for both H5N1 and H7N9. Our results enable us to predict age distributions of severe disease for future pandemics and demonstrate that a novel strain's pandemic potential increases yearly when a group-mismatched HA subtype dominates seasonal influenza circulation. These findings open new frontiers for rational pandemic risk assessment

Practical considerations for measuring the effective reproductive number, Rt

Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation.ISSN:1553-734XISSN:1553-735

Childhood immune imprinting to influenza A shapes birth year-specific risk during seasonal H1N1 and H3N2 epidemics.

Across decades of co-circulation in humans, influenza A subtypes H1N1 and H3N2 have caused seasonal epidemics characterized by different age distributions of cases and mortality. H3N2 causes the majority of severe, clinically attended cases in high-risk elderly cohorts, and the majority of overall deaths, whereas H1N1 causes fewer deaths overall, and cases shifted towards young and middle-aged adults. These contrasting age profiles may result from differences in childhood imprinting to H1N1 and H3N2 or from differences in evolutionary rate between subtypes. Here we analyze a large epidemiological surveillance dataset to test whether childhood immune imprinting shapes seasonal influenza epidemiology, and if so, whether it acts primarily via homosubtypic immune memory or via broader, heterosubtypic memory. We also test the impact of evolutionary differences between influenza subtypes on age distributions of cases. Likelihood-based model comparison shows that narrow, within-subtype imprinting shapes seasonal influenza risk alongside age-specific risk factors. The data do not support a strong effect of evolutionary rate, or of broadly protective imprinting that acts across subtypes. Our findings emphasize that childhood exposures can imprint a lifelong immunological bias toward particular influenza subtypes, and that these cohort-specific biases shape epidemic age distributions. As a consequence, newer and less "senior" antibody responses acquired later in life do not provide the same strength of protection as responses imprinted in childhood. Finally, we project that the relatively low mortality burden of H1N1 may increase in the coming decades, as cohorts that lack H1N1-specific imprinting eventually reach old age

Estimated effectiveness of traveller screening to prevent international spread of 2019 novel coronavirus (2019-nCoV).

Traveller screening is being used to limit further global spread of 2019 novel coronavirus (nCoV) following its recent emergence. Here, we project the impact of different travel screening programs given remaining uncertainty around the values of key nCoV life history and epidemiological parameters. Even under best-case assumptions, we estimate that screening will miss more than half of infected travellers. Breaking down the factors leading to screening successes and failures, we find that most cases missed by screening are fundamentally undetectable, because they have not yet developed symptoms and are unaware they were exposed. These findings emphasize the need for measures to track travellers who become ill after being missed by a travel screening program. We make our model available for interactive use so stakeholders can explore scenarios of interest using the most up-to-date information. We hope these findings contribute to evidence-based policy to combat the spread of nCoV, and to prospective planning to mitigate future emerging pathogens