Identifying beliefs driving COVID-19 vaccination: Lessons for effective messaging

Increasing vaccination acceptance has been essential during the COVID-19 pandemic and in preparation for future public health emergencies. This study aimed to identify messaging strategies to encourage vaccine uptake by measuring the drivers of COVID-19 vaccination among the general public. A survey to assess COVID-19 vaccination acceptance and hesitancy was advertised on Facebook in February-April 2022. The survey included items asking about COVID-19 vaccination status and participant demographics, and three scales assessing medical mistrust, perceived COVID-19 risk, and COVID-19 vaccine confidence (adapted from the Oxford COVID-19 vaccine confidence and complacency scale). The main outcome was vaccination, predicted by patient demographics and survey scale scores. Of 1,915 survey responses, 1,450 (75.7%) were included, with 1,048 (72.3%) respondents reporting they had been vaccinated. In a multivariable regression model, the COVID-19 vaccine confidence scale was the strongest predictor of vaccination, along with education level and perceived COVID-19 risk. Among the items on this scale, not all were equally important in predicting COVID-19 vaccination. The items that best predicted vaccination, at a given score on the COVID-19 vaccine confidence scale, included confidence that vaccine side effects are minimal, that the vaccine will work, that the vaccine will help the community, and that the vaccine provides freedom to move on with life. This study improved our understanding of perceptions most strongly associated with vaccine acceptance, allowing us to consider how to develop messages that may be particularly effective in encouraging vaccination among the general public for both the COVID-19 pandemic and future public health emergencies.

A Potential Outcomes Approach to Selection Bias

We propose a novel definition of selection bias in analytic epidemiology using potential outcomes. This definition captures selection bias under both the structural approach (where conditioning on selection into the study opens a noncausal path from exposure to disease in a directed acyclic graph) and the traditional definition (where a given measure of association differs between the study sample and the population eligible for inclusion). This approach is nonparametric, and selection bias under the approach can be analyzed using single-world intervention graphs both under and away from the null hypothesis. It allows the simultaneous analysis of confounding and selection bias, it explicitly links the selection of study participants to the estimation of causal effects using study data, and it can be adapted to handle selection bias in descriptive epidemiology. Through examples, we show that this approach provides a novel perspective on the variety of mechanisms that can generate selection bias and simplifies the analysis of selection bias in matched studies and case-cohort studies.

Catch the tweet to fight the flu: Using Twitter to promote flu shots on a college campus

Objective: Over the 2018-2019 flu season we conducted a randomized controlled trial examining the efficacy of a Twitter campaign on vaccination rates. Concurrently we investigated potential interactions between digital social network structure and vaccination status. Participants: Undergratuates at a large midwestern public university were randomly assigned to an intervention (n = 353) or control (n = 349) group. Methods: Vaccination data were collected via monthly surveys. Participant Twitter data were collected through the public-facing Twitter API. Intervention impact was assessed with logistic regression. Standard network science tools examined vaccination coverage over online social networks. Results: The campaign had no effect on vaccination outcome. Receiving a flu shot the prior year had a positive impact on participant vaccination. Evidence of an interaction between digital social network structure and vaccination status was detected. Conclusions: Social media campaigns may not be sufficient for increasing vaccination rates. There may be potential for social media campaigns that leverage network structure.

Rothman diagrams: the geometry of causal inference in epidemiology

Here, we explain and illustrate a geometric perspective on causal inference in cohort studies that can help epidemiologists understand the role of standardization in causal inference as well as the distinctions between confounding, effect modification, and noncollapsibility. For simplicity, we focus on a binary exposure X, a binary outcome D, and a binary confounder C that is not causally affected by X. Rothman diagrams plot risk in the unexposed on the x-axis and risk in the exposed on the y-axis. The crude risks define one point in the unit square, and the stratum-specific risks define two other points in the unit square. These three points can be used to identify confounding and effect modification, and we show briefly how these concepts generalize to confounders with more than two levels. We propose a simplified but equivalent definition of collapsibility in terms of standardization, and we show that a measure of association is collapsible if and only if all of its contour lines are straight. We illustrate these ideas using data from a study conducted in Newcastle upon Tyne, United Kingdom, where the causal effect of smoking on 20-year mortality was confounded by age. We conclude that causal inference should be taught using geometry before using regression models.

Necessary and sufficient conditions for exact closures of epidemic equations on configuration model networks

We prove that it is possible to obtain the exact closure of SIR pairwise epidemic equations on a configuration model network if and only if the degree distribution follows a Poisson, binomial, or negative binomial distribution. The proof relies on establishing the equivalence, for these specific degree distributions, between the closed pairwise model and a dynamical survival analysis (DSA) model that was previously shown to be exact. Specifically, we demonstrate that the DSA model is equivalent to the well-known edge-based Volz model. Using this result, we also provide reductions of the closed pairwise and Volz models to a single equation that involves only susceptibles. This equation has a useful statistical interpretation in terms of times to infection. We provide some numerical examples to illustrate our results.

Infection-Induced Immunity Is Associated With Protection Against Severe Acute Respiratory Syndrome Coronavirus 2 Infection and Decreased Infectivity

BACKGROUND

The impact of infection-induced immunity on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has not been well established. Here we estimate the effects of prior infection induced immunity in adults and children on SARS-CoV-2 transmission in households.

METHODS

We conducted a household cohort study from March 2020-November 2022 in Managua, Nicaragua; following a housheold SARS-CoV-2 infection, household members are closely monitored for infection. We estimate the association of time period, age, symptoms, and prior infection with secondary attack risk.

RESULTS

Overall, transmission occurred in 70.2% of households, 40.9% of household contacts were infected, and the secondary attack risk ranged from 8.1% to 13.9% depending on the time period. Symptomatic infected individuals were more infectious (rate ratio [RR] 21.2, 95% confidence interval [CI]: 7.4-60.7) and participants with a prior infection were half as likely to be infected compared to naïve individuals (RR 0.52, 95% CI:.38-.70). In models stratified by age, prior infection was associated with decreased infectivity in adults and adolescents (secondary attack risk [SAR] 12.3, 95% CI: 10.3, 14.8 vs 17.5, 95% CI: 14.8, 20.7). However, although young children were less likely to transmit, neither prior infection nor symptom presentation was associated with infectivity. During the Omicron era, infection-induced immunity remained protective against infection.

CONCLUSIONS

Infection-induced immunity is associated with decreased infectivity for adults and adolescents. Although young children are less infectious, prior infection and asymptomatic presentation did not reduce their infectivity as was seen in adults. As SARS-CoV-2 transitions to endemicity, children may become more important in transmission dynamics.

COVID-19 dynamics in an Ohio prison

Incarcerated individuals are a highly vulnerable population for infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Understanding the transmission of respiratory infections within prisons and between prisons and surrounding communities is a crucial component of pandemic preparedness and response. Here, we use mathematical and statistical models to analyze publicly available data on the spread of SARS-CoV-2 reported by the Ohio Department of Rehabilitation and Corrections (ODRC). Results from mass testing conducted on April 16, 2020 were analyzed together with time of first reported SARS-CoV-2 infection among Marion Correctional Institution (MCI) inmates. Extremely rapid, widespread infection of MCI inmates was reported, with nearly 80% of inmates infected within 3 weeks of the first reported inmate case. The dynamical survival analysis (DSA) framework that we use allows the derivation of explicit likelihoods based on mathematical models of transmission. We find that these data are consistent with three non-exclusive possibilities: (i) a basic reproduction number >14 with a single initially infected inmate, (ii) an initial superspreading event resulting in several hundred initially infected inmates with a reproduction number of approximately three, or (iii) earlier undetected circulation of virus among inmates prior to April. All three scenarios attest to the vulnerabilities of prisoners to COVID-19, and the inability to distinguish among these possibilities highlights the need for improved infection surveillance and reporting in prisons.

Projecting COVID-19 cases and hospital burden in Ohio

As the Coronavirus 2019 disease (COVID-19) started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at The Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: (1) A Dynamical Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification. (2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology is also made publicly available. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

The COVID-19 vaccine concerns scale: Development and validation of a new measure

Reasons for COVID-19 hesitancy are multi-faceted and tend to differ from those for general vaccine hesitancy. We developed the COVID-19 Vaccine Concerns Scale (CVCS), a self-report measure intended to better understand individuals’ concerns about COVID-19 vaccines. We validated the scale using data from a convenience sample of 2,281 emergency medical services providers, a group of professionals with high occupational COVID-19 risk. Measures included the CVCS items, an adapted Oxford COVID-19 vaccine hesitancy scale, a general vaccine hesitancy scale, demographics, and self-reported COVID-19 vaccination status. The CVCS had high internal consistency reliability (α = .89). A one-factor structure was determined by exploratory and confirmatory factor analyses (EFA and CFA), resulting in a seven-item scale. The model had good fit (X2[14] = 189.26, p < .001; CFI = .95, RMSEA = .11 [.09, .12], NNFI = .93, SRMR = .03). Moderate Pearson correlations with validated scales of general vaccine hesitancy (r = .71 , p < .001; n = 2144) and COVID-19 vaccine hesitancy (r = .82; p < .001; n = 2279) indicated construct validity. The CVCS predicted COVID-19 vaccination status (B =  −2.21, Exp(B) = .11 [95% CI = .09, .13], Nagelkerke R² = .55), indicating criterion-related validity. In sum, the 7-item CVCS is a reliable and valid self-report measure to examine fears and concerns about COVID-19 vaccines. The scale predicts COVID-19 vaccination status and can be used to inform efforts to reduce COVID-19 vaccine hesitancy.

Shortening Duration of Swine Exhibitions to Reduce Risk for Zoonotic Transmission of Influenza A Virus

Reducing zoonotic influenza A virus (IAV) risk in the United States necessitates mitigation of IAV in exhibition swine. We evaluated the effectiveness of shortening swine exhibitions to <72 hours to reduce IAV risk. We longitudinally sampled every pig daily for the full duration of 16 county fairs during 2014-2015 (39,768 nasal wipes from 6,768 pigs). In addition, we estimated IAV prevalence at 195 fairs during 2018-2019 to test the hypothesis that <72-hour swine exhibitions would have lower IAV prevalence. In both studies, we found that shortening duration drastically reduces IAV prevalence in exhibition swine at county fairs. Reduction of viral load in the barn within a county fair is critical to reduce the risk for interspecies IAV transmission and pandemic potential. Therefore, we encourage fair organizers to shorten swine shows to protect the health of both animals and humans.

Commentary on the use of the reproduction number R during the COVID-19 pandemic

Since the beginning of the COVID-19 pandemic, the reproduction number [Formula: see text] has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic, [Formula: see text] is defined as the average number of secondary infections caused by one primary infected individual. [Formula: see text] seems convenient, because the epidemic is expanding if [Formula: see text] and contracting if [Formula: see text]. The magnitude of [Formula: see text] indicates by how much transmission needs to be reduced to control the epidemic. Using [Formula: see text] in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition of [Formula: see text] but many, and the precise definition of [Formula: see text] affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined [Formula: see text], there may be different statistical methods used to estimate its value, and the choice of method will affect the estimate. (3) The availability and type of data used to estimate [Formula: see text] vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when [Formula: see text] is useful, when it may be of use but needs to be interpreted with care, and when it may be an inappropriate indicator of the progress of the epidemic. We also argue that careful definition of [Formula: see text], and the data and methods used to estimate it, can make [Formula: see text] a more useful metric for future management of the epidemic.

Projecting COVID-19 Cases and Subsequent Hospital Burden in Ohio

As the Coronavirus 2019 (COVID-19) disease started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at the Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: 1) A Dynamic Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification. 2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology has been made available publicly.

Highlights

We present a novel statistical approach called Dynamic Survival Analysis (DSA) to model an epidemic curve with incomplete data. The DSA approach is advantageous over standard statistical methods primarily because it does not require prior knowledge of the size of the susceptible population, the overall prevalence of the disease, and also the shape of the epidemic curve.The principal motivation behind the study was to obtain predictions of case counts of COVID-19 and the resulting hospital burden in the state of Ohio during the early phase of the pandemic.The proposed methodology was applied to the COVID-19 incidence data in the state of Ohio to support the Ohio Department of Health (ODH) and the Ohio Hospital Association (OHA) with predictions of hospital burden in each of the Hospital Catchment Areas (HCAs) of the state.

Dynamic survival analysis for non-Markovian epidemic models

We present a new method for analysing stochastic epidemic models under minimal assumptions. The method, dubbed dynamic survival analysis (DSA), is based on a simple yet powerful observation, namely that population-level mean-field trajectories described by a system of partial differential equations may also approximate individual-level times of infection and recovery. This idea gives rise to a certain non-Markovian agent-based model and provides an agent-level likelihood function for a random sample of infection and/or recovery times. Extensive numerical analyses on both synthetic and real epidemic data from foot-and-mouth disease in the UK (2001) and COVID-19 in India (2020) show good accuracy and confirm the method’s versatility in likelihood-based parameter estimation. The accompanying software package gives prospective users a practical tool for modelling, analysing and interpreting epidemic data with the help of the DSA approach.

Mission, Organization and Future Direction of the Serological Sciences Network for COVID-19 (SeroNet) Epidemiologic Cohort Studies

Global efforts are needed to elucidate the epidemiology of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the underlying cause of coronavirus disease 2019 (COVID-19) including seroprevalence, risk factors and long-term sequelae, as well as immune responses following vaccination across populations and the social dimensions of prevention and treatment strategies. In the U.S., the National Cancer Institute in partnership with the National Institute of Allergy and Infectious Diseases, established the SARS-CoV-2 Serological Sciences Network (SeroNet) as the nation’s largest coordinated effort to study COVID-19. The network is comprised of multidisciplinary researchers bridging gaps and fostering collaborations between immunologists, epidemiologists, virologists, clinicians and clinical laboratories, social and behavioral scientists, policy makers, data scientists, and community members. In total, 49 institutions form the SeroNet consortium to study individuals with cancer, autoimmune disease, inflammatory bowel diseases, cardiovascular diseases, HIV, transplant recipients, as well as otherwise healthy pregnant women, children, college students, and high-risk occupational workers (including health care workers and first responders). Several studies focus on underrepresented populations, including ethnic minorities and rural communities. To support integrative data analyses across SeroNet studies, efforts are underway to define common data elements for standardized serology measurements, cellular and molecular assays, self-reported data, treatment, and clinical outcomes. In this paper, we discuss the overarching framework for SeroNet epidemiology studies, critical research questions under investigation, and data accessibility for the worldwide scientific community. Lessons learned will help inform preparedness and responsiveness to future emerging diseases.

Closing the Gap on COVID-19 Vaccinations in First Responders and Beyond: Increasing Trust

Although COVID-19 vaccines are widely available in the U.S. and much of the world, many have chosen to forgo this vaccination. Emergency medical services (EMS) professionals, despite their role on the frontlines and interactions with COVID-positive patients, are not immune to vaccine hesitancy. Via a survey conducted in April 2021, we investigated the extent to which first responders in the U.S. trusted various information sources to provide reliable information about COVID-19 vaccines. Those vaccinated generally trusted healthcare providers as a source of information, but unvaccinated first responders had fairly low trust in this information source-a group to which they, themselves, belong. Additionally, regardless of vaccination status, trust in all levels of government, employers, and their community as sources of information was low. Free-response explanations provided some context to these findings, such as preference for other COVID-19 management options, including drugs proven ineffective. A trusted source of COVID-19 vaccination information is not readily apparent. Individuals expressed a strong desire for the autonomy to make vaccination decisions for themselves, as opposed to mandates. Potential reasons for low trust, possible solutions to address them, generalizability to the broader public, and implications of low trust in official institutions are discussed.

COVID-19 Vaccinations in EMS Professionals: Prevalence and Predictors

Background:

Immunizations for emergency medical services (EMS) professionals during pandemics are an important tool to increase the safety of the workforce as well as their patients. The purpose of this study was to better understand EMS professionals’ decisions to receive or decline a COVID-19 vaccine.

Methods:

We conducted a cross-sectional analysis of nationally certified EMS professionals (18–85 years) in April 2021. Participants received an electronic survey asking whether they received a vaccine, why or why not, and their associated beliefs using three validated scales: perceived risk of COVID-19, medical mistrust, and confidence in the COVID-19 vaccine. Data were merged with National Registry dataset demographics. Analyses included descriptive analysis and multivariable logistic regression (OR, 95% CI). Multivariate imputation by chained equations was used for missingness.

Results:

A total of 2,584 respondents satisfied inclusion criteria (response rate = 14%). Overall, 70% of EMS professionals were vaccinated. Common reasons for vaccination among vaccinated respondents were to protect oneself (76%) and others (73%). Common reasons for non-vaccination among non-vaccinated respondents included concerns about vaccine safety (53%) and beliefs that vaccination was not necessary (39%). Most who had not received the vaccine did not plan to get it in the future (84%). Hesitation was most frequently related to wanting to see how the vaccine was working for others (55%). Odds of COVID-19 vaccination were associated with demographics including age (referent <28 years; 39–50 years: 1.56, 1.17–2.08; >51 years: 2.22, 1.64–3.01), male sex (1.26, 1.01–1.58), residing in an urban/suburban area (referent rural; 1.36, 1.08–1.70), advanced education (referent GED/high school and below; bachelor’s and above: 1.72, 1.19–2.47), and working at a hospital (referent fire-based agency; 1.53, 1.04–2.24). Additionally, vaccination odds were significantly higher with greater perceived risk of COVID-19 (2.05, 1.68–2.50), and higher vaccine confidence (2.84, 2.40–3.36). Odds of vaccination were significantly lower with higher medical mistrust (0.54, 0.46–0.63).

Conclusion:

Despite vaccine availability, not all EMS professionals had been vaccinated. The decision to receive a COVID-19 vaccine was associated with demographics, beliefs regarding COVID-19 and the vaccine, and medical mistrust. Efforts to increase COVID-19 vaccination rates should emphasize the safety and efficacy of vaccines.

Influenza Vaccination of Swine Reduces Public Health Risk at the Swine-Human Interface

Influenza A viruses (IAV) in swine (IAV-S) pose serious risk to public health through spillover at the human-animal interface. Continued zoonotic transmission increases the likelihood novel IAV-S capable of causing the next influenza pandemic will emerge from this animal reservoir. Because current mitigation strategies are insufficient to prevent IAV zoonosis, we investigated the ability of swine vaccination to decrease IAV-S zoonotic transmission risk. We assessed postchallenge viral shedding in market-age swine vaccinated with either live-attenuated influenza virus (LAIV), killed influenza virus (KV), or sham vaccine (NV). We also assessed postchallenge transmission by exposing naive ferrets to pigs with contact types reflective of those experienced by humans in a field setting. LAIV and KV swine groups exhibited a nearly 100-fold reduction in peak nasal titer (LAIV mean, 4.55 log 50% tissue culture infectious dose [TCID50]/ml; KV mean, 4.53 log TCID50/ml) compared to NV swine (mean, 6.40 log TCID50/ml). Air sampling during the postchallenge period revealed decreased cumulative IAV in LAIV and KV study room air (LAIV, area under the concentration-time curve [AUC] of 57.55; KV, AUC = 24.29) compared to the NV study room (AUC = 86.92). Pairwise survival analysis revealed a significant delay in onset of infection among ferrets exposed to LAIV pigs versus NV pigs (rate ratio, 0.66; P = 0.028). Ferrets exposed to vaccinated pigs had lower cumulative virus titers in nasal wash samples (LAIV versus NV, P < 0.0001; KV versus NV, P= 0.3490) and experienced reduced clinical signs during infection. Our findings support the implementation of preexhibition influenza vaccination of swine to reduce the public health risk posed by IAV-S at agricultural exhibitions. IMPORTANCE Swine exhibited at agricultural fairs in North America have been the source of repeated zoonotic influenza A virus transmission, which creates a pathway for influenza pandemic emergence. We investigated the effect of using either live-attenuated influenza virus or killed influenza virus vaccines as prefair influenza vaccination of swine on zoonotic influenza transmission risk. Ferrets were exposed to the pigs in order to simulate human exposure in a field setting. We observed reductions in influenza A virus shedding in both groups of vaccinated pigs as well as the corresponding ferret exposure groups, indicating vaccination improved outcomes on both sides of the interface. There was also significant delay in onset of infection among ferrets that were exposed to live-attenuated virus-vaccinated pigs, which might be beneficial during longer fairs. Our findings indicate that policies mandating influenza vaccination of swine before fairs, while not currently common, would reduce the public health risk posed by influenza zoonosis.

Incorporating age and delay into models for biophysical systems

In many biological systems, chemical reactions or changes in a physical state are assumed to occur instantaneously. For describing the dynamics of those systems, Markov models that require exponentially distributed inter-event times have been used widely. However, some biophysical processes such as gene transcription and translation are known to have a significant gap between the initiation and the completion of the processes, which renders the usual assumption of exponential distribution untenable. In this paper, we consider relaxing this assumption by incorporating age-dependent random time delays (distributed according to a given probability distribution) into the system dynamics. We do so by constructing a measure-valued Markov process on a more abstract state space, which allows us to keep track of the 'ages' of molecules participating in a chemical reaction. We study the large-volume limit of such age-structured systems. We show that, when appropriately scaled, the stochastic system can be approximated by a system of partial differential equations (PDEs) in the large-volume limit, as opposed to ordinary differential equations (ODEs) in the classical theory. We show how the limiting PDE system can be used for the purpose of further model reductions and for devising efficient simulation algorithms. In order to describe the ideas, we use a simple transcription process as a running example. We, however, note that the methods developed in this paper apply to a wide class of biophysical systems.

Effectiveness of social and behavioral change communication intervention to promote the use of 7.1% chlorhexidine for umbilical cord care in hard-to-reach rural Bangladesh: A mixed method study

Background

Developing countries account for 99.0% of the 2.7 million neonatal deaths occurring worldwide each year. Umbilical cord infection contributes greatly to this predicament, but evidence shows that 7.1% chlorhexidine solution (CHX) can substantially reduce the risk of infection. To address this challenge, this study aimed to determine the effect of a social and behavioral change communication (SBCC) intervention on promoting the use of WHO recommended CHX as well as on improving the knowledge, attitude, and practices of rural communities regarding umbilical cord care in hard-to-reach areas of Bangladesh.

Methods

A pretest-posttest quasi-experimental study was conducted in two unions of Jamalpur district during 2017-2019 among 748 pregnant women in their third trimester. The SBCC intervention was implemented through town-hall meetings (n = 3), community meetings (n = 30), and door-to-door meetings (n = 22 223) in Dangdhora union, which served as the intervention group, while Hativanga union was kept as a real-time comparator group. Qualitative data were collected from a total of 200 respondents, where 100 participants were chosen from both intervention and control groups. Statistical analysis was carried out in R and outcomes with P values less than 0.05 at 95% confidence intervals (CIs) were presented.

Results

Following SBCC intervention, significant (P < 0.001) improvements were observed in the intervention group with regards to the primary objective: CHX use increased from 1.07% to 57.80%, while CHX use decreased from 1.6% to 0.0% in the control group. Meaningful improvements were also observed in relation to knowledge (29.0% to 43.0%), attitude (53.0% to 90.0%), and practices (25.0% to 70.0%) of rural communities regarding cord care. Marked improvements were also observed in the intervention group related to understanding causes of cord infections; importance of cord cleanliness; use of antiseptic and other preventive measures; care-seeking behavior; and ensuring hygienic childbirth.

Conclusions

This pioneer study revealed that SBCC interventions led to an increase in CHX use and improved the knowledge, attitude and practices of Bangladeshi communities regarding cord care and cord infection. This indicates that SBCC intervention is indeed an effective and feasible method for reducing infant mortality rates in hard-to-reach populations and achieving SDG goal 3.2.

Estimating and interpreting secondary attack risk: Binomial considered biased

The household secondary attack risk (SAR), often called the secondary attack rate or secondary infection risk, is the probability of infectious contact from an infectious household member A to a given household member B, where we define infectious contact to be a contact sufficient to infect B if he or she is susceptible. Estimation of the SAR is an important part of understanding and controlling the transmission of infectious diseases. In practice, it is most often estimated using binomial models such as logistic regression, which implicitly attribute all secondary infections in a household to the primary case. In the simplest case, the number of secondary infections in a household with m susceptibles and a single primary case is modeled as a binomial(m, p) random variable where p is the SAR. Although it has long been understood that transmission within households is not binomial, it is thought that multiple generations of transmission can be neglected safely when p is small. We use probability generating functions and simulations to show that this is a mistake. The proportion of susceptible household members infected can be substantially larger than the SAR even when p is small. As a result, binomial estimates of the SAR are biased upward and their confidence intervals have poor coverage probabilities even if adjusted for clustering. Accurate point and interval estimates of the SAR can be obtained using longitudinal chain binomial models or pairwise survival analysis, which account for multiple generations of transmission within households, the ongoing risk of infection from outside the household, and incomplete follow-up. We illustrate the practical implications of these results in an analysis of household surveillance data collected by the Los Angeles County Department of Public Health during the 2009 influenza A (H1N1) pandemic.

The household secondary attack risk (SAR), often called the secondary attack rate or secondary infection risk, is the probability of infectious contact from an infectious household member A to a given household member B, where we define infectious contact to be a contact sufficient to infect B if he or she is susceptible. The most common statistical models used to estimate the SAR are binomial models such as logistic regression, which implicitly assume that all secondary infections in a household are infected by the primary case. Here, we use analytical calculations and simulations to show that estimation of the SAR must account for multiple generations of transmission within households. As an example, we show that binomial models and statistical models that account for multiple generations of within-household transmission reach different conclusions about the household SAR for 2009 influenza A (H1N1) in Los Angeles County, with the latter models fitting the data better. In an epidemic, accurate estimation of the SAR allows rigorous evaluation of the effectiveness of public health interventions such as social distancing, prophylaxis or treatment, and vaccination.

Household Secondary Attack Rate of COVID-19 and Associated Determinants

BACKGROUND

As of April 2, 2020, the global reported number of COVID-19 cases has crossed over 1 million with more than 55,000 deaths. The household transmissibility of SARS-CoV-2, the causative pathogen, remains elusive.

METHODS

Based on a comprehensive contact-tracing dataset from Guangzhou, we estimated both the population-level effective reproductive number and individual-level secondary attack rate (SAR) in the household setting. We assessed age effects on transmissibility and the infectivity of COVID-19 cases during their incubation period.

RESULTS

A total of 195 unrelated clusters with 212 primary cases, 137 nonprimary (secondary or tertiary) cases and 1938 uninfected close contacts were traced. We estimated the household SAR to be 13.8% (95% CI: 11.1-17.0%) if household contacts are defined as all close relatives and 19.3% (95% CI: 15.5-23.9%) if household contacts only include those at the same residential address as the cases, assuming a mean incubation period of 4 days and a maximum infectious period of 13 days. The odds of infection among children (<20 years old) was only 0.26 (95% CI: 0.13-0.54) times of that among the elderly (≥60 years old). There was no gender difference in the risk of infection. COVID-19 cases were at least as infectious during their incubation period as during their illness. On average, a COVID-19 case infected 0.48 (95% CI: 0.39-0.58) close contacts. Had isolation not been implemented, this number increases to 0.62 (95% CI: 0.51-0.75). The effective reproductive number in Guangzhou dropped from above 1 to below 0.5 in about 1 week.

CONCLUSION

SARS-CoV-2 is more transmissible in households than SARS-CoV and MERS-CoV, and the elderly ≥60 years old are the most vulnerable to household transmission. Case finding and isolation alone may be inadequate to contain the pandemic and need to be used in conjunction with heightened restriction of human movement as implemented in Guangzhou.

Survival dynamical systems: individual-level survival analysis from population-level epidemic models

In this paper, we show that solutions to ordinary differential equations describing the large-population limits of Markovian stochastic epidemic models can be interpreted as survival or cumulative hazard functions when analysing data on individuals sampled from the population. We refer to the individual-level survival and hazard functions derived from population-level equations as a survival dynamical system (SDS). To illustrate how population-level dynamics imply probability laws for individual-level infection and recovery times that can be used for statistical inference, we show numerical examples based on synthetic data. In these examples, we show that an SDS analysis compares favourably with a complete-data maximum-likelihood analysis. Finally, we use the SDS approach to analyse data from a 2009 influenza A(H1N1) outbreak at Washington State University.

Modeling outbreak data: Analysis of a 2012 Ebola virus disease epidemic in DRC

We describe two approaches to modeling data from a small to moderate-sized epidemic outbreak. The first approach is based on a branching process approximation and direct analysis of the transmission network, whereas the second one is based on a survival model derived from the classical SIR equations with no explicit transmission information. We compare these approaches using data from a 2012 outbreak of Ebola virus disease caused by Bundibugyo ebolavirus in city of Isiro, Democratic Republic of the Congo. The branching process model allows for a direct comparison of disease transmission across different environments, such as the general community or the Ebola treatment unit. However, the survival model appears to yield parameter estimates with more accuracy and better precision in some circumstances.

Investigating Rare Risk Factors for Nipah Virus in Bangladesh: 2001-2012

Human Nipah encephalitis outbreaks have been identified almost yearly in Bangladesh since 2001. Though raw date palm sap consumption and person-to-person contact are recognized as major transmission pathways, alternative pathways of transmission are plausible and may not have been identified due to limited statistical power in each outbreak. We conducted a risk factor analysis using all 157 cases and 632 controls surveyed in previous investigations during 2004-2012 to identify exposures independently associated with Nipah, since date palm sap was first asked about as an exposure in 2004. To further explore possible rare exposures, we also conducted in-depth interviews with all cases, or proxies, since 2001 that reported no exposure to date palm sap or contact with another case. Cases were 4.9 (95% 3.2-7.7) times more likely to consume raw date palm sap and 7.3 (95% 4.0-13.4) times more likely to have contact with a Nipah case than controls. In-depth interviews revealed that 39/182 (21%) of Nipah cases reporting neither date palm sap consumption nor contact with another case were misclassified. Prevention efforts should be focused on interventions to interrupt transmission through date palm sap consumption and person-to-person contact. Furthermore, pooling outbreak investigation data is a good method for assessing rare exposures.

The epidemiology and transmissibility of Zika virus in Girardot and San Andres island, Colombia, September 2015 to January 2016

Transmission of Zika virus (ZIKV) was first detected in Colombia in September 2015. As of April 2016, Colombia had reported over 65,000 cases of Zika virus disease (ZVD). We analysed daily surveillance data of ZVD cases reported to the health authorities of San Andres and Girardot, Colombia, between September 2015 and January 2016. ZVD was laboratory-confirmed by reverse transcription-polymerase chain reaction (RT-PCR) in the serum of acute cases within five days of symptom onset. We use daily incidence data to estimate the basic reproductive number (R0) in each population. We identified 928 and 1,936 reported ZVD cases from San Andres and Girardot, respectively. The overall attack rate for reported ZVD was 12.13 cases per 1,000 residents of San Andres and 18.43 cases per 1,000 residents of Girardot. Attack rates were significantly higher in females in both municipalities (p < 0.001). Cases occurred in all age groups with highest rates in 20 to 49 year-olds. The estimated R0 for the Zika outbreak was 1.41 (95% confidence interval (CI): 1.15-1.74) in San Andres and 4.61 (95% CI: 4.11-5.16) in Girardot. Transmission of ZIKV is ongoing in the Americas. The estimated R0 from Colombia supports the observed rapid spread.

Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees

Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks. However, transmission trees from one outbreak do not generalize to future outbreaks. Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about disease transmission. In a survival analysis framework, estimation of transmission parameters is based on sums or averages over the possible transmission trees. A phylogeny can increase the precision of these estimates by providing partial information about who infected whom. The leaves of the phylogeny represent sampled pathogens, which have known hosts. The interior nodes represent common ancestors of sampled pathogens, which have unknown hosts. Starting from assumptions about disease biology and epidemiologic study design, we prove that there is a one-to-one correspondence between the possible assignments of interior node hosts and the transmission trees simultaneously consistent with the phylogeny and the epidemiologic data on person, place, and time. We develop algorithms to enumerate these transmission trees and show these can be used to calculate likelihoods that incorporate both epidemiologic data and a phylogeny. A simulation study confirms that this leads to more efficient estimates of hazard ratios for infectiousness and baseline hazards of infectious contact, and we use these methods to analyze data from a foot-and-mouth disease virus outbreak in the United Kingdom in 2001. These results demonstrate the importance of data on individuals who escape infection, which is often overlooked. The combination of survival analysis and algorithms linking phylogenies to transmission trees is a rigorous but flexible statistical foundation for molecular infectious disease epidemiology.

Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks. However, transmission trees from one outbreak do not generalize to future outbreaks. Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about disease transmission. Accurate estimates of transmission parameters can help identify risk factors for transmission and aid the design and evaluation of public health interventions for emerging infections. Using statistical methods for time-to-event data (survival analysis), estimation of transmission parameters is based on sums or averages over the possible transmission trees. By providing partial information about who infected whom, a pathogen phylogeny can reduce the set of possible transmission trees and increase the precision of transmission parameter estimates. We derive algorithms that enumerate the transmission trees consistent with a pathogen phylogeny and epidemiologic data, show how to calculate likelihoods for transmission data with a phylogeny, and apply these methods to a foot and mouth disease outbreak in the United Kingdom in 2001. These methods will allow pathogen genetic sequences to be incorporated into the analysis of outbreak investigations, vaccine trials, and other studies of infectious disease transmission.

Semiparametric Relative-risk Regression for Infectious Disease Transmission Data

This paper introduces semiparametric relative-risk regression models for infectious disease data. The units of analysis in these models are pairs of individuals at risk of transmission. The hazard of infectious contact from i to j consists of a baseline hazard multiplied by a relative risk function that can be a function of infectiousness covariates for i, susceptibliity covariates for j, and pairwise covariates. When who-infects-whom is observed, we derive a profile likelihood maximized over all possible baseline hazard functions that is similar to the Cox partial likelihood. When who-infects-whom is not observed, we derive an EM algorithm to maximize the profile likelihood integrated over all possible combinations of who-infected-whom. This extends the most important class of regression models in survival analysis to infectious disease epidemiology. These methods can be implemented in standard statistical software, and they will be able to address important scientific questions about emerging infectious diseases with greater clarity, flexibility, and rigor than current statistical methods allow.

Household transmissibility of avian influenza A (H7N9) virus, China, February to May 2013 and October 2013 to March 2014

To study human-to-human transmissibility of the avian influenza A (H7N9) virus in China, household contact information was collected for 125 index cases during the spring wave (February to May 2013), and for 187 index cases during the winter wave (October 2013 to March 2014). Using a statistical model, we found evidence for human-to-human transmission, but such transmission is not sustainable. Under plausible assumptions about the natural history of disease and the relative transmission frequencies in settings other than household, we estimate the household secondary attack rate (SAR) among humans to be 1.4% (95% CI: 0.8 to 2.3), and the basic reproductive number R0 to be 0.08 (95% CI: 0.05 to 0.13). The estimates range from 1.3% to 2.2% for SAR and from 0.07 to 0.12 for R0 with reasonable changes in the assumptions. There was no significant change in the human-to-human transmissibility of the virus between the two waves, although a minor increase was observed in the winter wave. No sex or age difference in the risk of infection from a human source was found. Human-to-human transmissibility of H7N9 continues to be limited, but it needs to be closely monitored for potential increase via genetic reassortment or mutation.

A pathway EM-algorithm for estimating vaccine efficacy with a non-monotone validation set

Here, we consider time-to-event data where individuals can experience two or more types of events that are not distinguishable from one another without further confirmation, perhaps by laboratory test. The event type of primary interest can occur only once. The other types of events can recur. If the type of a portion of the events is identified, this forms a validation set. However, even if a random sample of events are tested, confirmations can be missing nonmonotonically, creating uncertainty about whether an individual is still at risk for the event of interest. For example, in a study to estimate efficacy of an influenza vaccine, an individual may experience a sequence of symptomatic respiratory illnesses caused by various pathogens over the season. Often only a limited number of these episodes are confirmed in the laboratory to be influenza-related or not. We propose two novel methods to estimate covariate effects in this survival setting, and subsequently vaccine efficacy. The first is a pathway expectation-maximization (EM) algorithm that takes into account all pathways of event types in an individual compatible with that individual's test outcomes. The pathway EM iteratively estimates baseline hazards that are used to weight possible event types. The second method is a non-iterative pathway piecewise validation method that does not estimate the baseline hazards. These methods are compared with a previous simpler method. Simulation studies suggest mean squared error is lower in the efficacy estimates when the baseline hazards are estimated, especially at higher hazard rates. We use the pathway EM-algorithm to reevaluate the efficacy of a trivalent live-attenuated influenza vaccine during the 2003-2004 influenza season in Temple-Belton, Texas, and compare our results with a previously published analysis.

Clinical and immunological aspects of post-kala-azar dermal leishmaniasis in Bangladesh

We conducted active surveillance for kala-azar and post-kala-azar dermal leishmaniasis (PKDL) in a population of 24,814 individuals. Between 2002 and 2010, 1,002 kala-azar and 185 PKDL cases occurred. Median PKDL patient age was 12 years; 9% had no antecedent kala-azar. Cases per 10,000 person-years peaked at 90 for kala-azar (2005) and 28 for PKDL (2007). Cumulative PKDL incidence among kala-azar patients was 17% by 5 years. Kala-azar patients younger than 15 years were more likely than older patients to develop PKDL; no other risk factors were identified. The most common lesions were hypopigmented macules. Of 98 untreated PKDL patients, 48 (49%) patients had resolution, with median time of 19 months. Kala-azar patients showed elevated interferon-γ (IFNγ), tumor necrosis factor-α (TNFα), and interleukin 10 (IL-10). Matrix metalloproteinase 9 (MMP9) and MMP9/tissue inhibitor of matrix metalloproteinase-1 (TIMP1) ratio were significantly higher in PKDL patients than in other groups. PKDL is frequent in Bangladesh and poses a challenge to the current visceral leishmaniasis elimination initiative in the Indian subcontinent.

Effect of recent diarrhoeal episodes on risk of pneumonia in children under the age of 5 years in Karachi, Pakistan

BACKGROUND

We assessed the association between the duration of diarrhoea and the risk ofpneumonia incidence among children <5 years of age.

METHODS

We analysed data from a cluster randomized controlled trial in Karachi, Pakistan, which assessed the effect of promoting hand washing with soap (antibacterial and plain) on child health. Field workers visited households with children <5 years of age weekly and asked primary caregivers if their child had diarrhoea, cough or difficulty breathing in the preceding week. We used the WHO clinical case definitions for diarrhoea and pneumonia.We used adjusted time-to-event analyses with cumulative diarrhoea prevalence over the previous 2 and 4 weeks as exposure and pneumonia as outcome. We calculated the attributable risk of pneumonia due to recent diarrhoea across the intervention groups.

RESULTS

873 households with children <5 years were visited. Children had an increased risk of pneumonia for every additional day of diarrhoea in the 2 weeks (1.06, 95% CI: 1.03-1.09) and 4 weeks (1.04, 95% CI: 1.03-1.06) prior to the week of pneumonia onset. The attributable risk of pneumonia cases due to recent exposure to diarrhoea was 6%. A lower associated pneumonia risk following diarrhoea was found in the control group: (3%) compared with soap groups (6% in antibacterial soap, 9% in plain soap).

CONCLUSION

Children <5 years of age are at an increased risk of pneumonia following recent diarrhoeal illness. Public health programmes that prevent diarrhoea may also reduce the burden of respiratory illnesses.

Nonparametric survival analysis of infectious disease data

This paper develops nonparametric methods based on contact intervals for the analysis of infectious disease data. The contact interval from person i to person j is the time between the onset of infectiousness in i and infectious contact from i to j, where we define infectious contact as a contact sufficient to infect a susceptible individual. The hazard function of the contact interval distribution equals the hazard of infectious contact from i to j, so it provides a summary of the evolution of infectiousness over time. When who-infects-whom is observed, the Nelson-Aalen estimator produces an unbiased estimate of the cumulative hazard function of the contact interval distribution. When who-infects-whom is not observed, we use an EM algorithm to average the Nelson-Aalen estimates from all possible combinations of who-infected-whom consistent with the observed data. This converges to a nonparametric maximum likelihood estimate of the cumulative hazard function that we call the marginal Nelson-Aalen estimate. We study the behavior of these methods in simulations and use them to analyze household surveillance data from the 2009 influenza A(H1N1) pandemic.

Date palm sap linked to Nipah virus outbreak in Bangladesh, 2008

INTRODUCTION

We investigated a cluster of patients with encephalitis in the Manikgonj and Rajbari Districts of Bangladesh in February 2008 to determine the etiology and risk factors for disease.

METHODS

We classified persons as confirmed Nipah cases by the presence of immunoglobulin M antibodies against Nipah virus (NiV), or by the presence of NiV RNA or by isolation of NiV from cerebrospinal fluid or throat swabs who had onset of symptoms between February 6 and March 10, 2008. We classified persons as probable cases if they reported fever with convulsions or altered mental status, who resided in the outbreak areas during that period, and who died before serum samples were collected. For the case-control study, we compared both confirmed and probable Nipah case-patients to controls, who were free from illness during the reference period. We used motion-sensor-infrared cameras to observe bat's contact of date palm sap.

RESULTS

We identified four confirmed and six probable case-patients, nine (90%) of whom died. The median age of the cases was 10 years; eight were males. The outbreak occurred simultaneously in two communities that were 44 km apart and separated by a river. Drinking raw date palm sap 2-12 days before illness onset was the only risk factor most strongly associated with the illness (adjusted odds ratio 25, 95% confidence intervals 3.3-∞, p<0.001). Case-patients reported no history of physical contact with bats, though community members often reported seeing bats. Infrared camera photographs showed that Pteropus bats frequently visited date palm trees in those communities where sap was collected for human consumption.

CONCLUSION

This is the second Nipah outbreak in Bangladesh where date palm sap has been implicated as the vehicle of transmission. Fresh date palm sap should not be drunk, unless effective steps have been taken to prevent bat access to the sap during collection.

Contact intervals, survival analysis of epidemic data, and estimation of R(0)

We argue that the time from the onset of infectiousness to infectious contact, which we call the "contact interval," is a better basis for inference in epidemic data than the generation or serial interval. Since contact intervals can be right censored, survival analysis is the natural approach to estimation. Estimates of the contact interval distribution can be used to estimate R(0) in both mass-action and network-based models. We apply these methods to 2 data sets from the 2009 influenza A(H1N1) pandemic.

Causes of early childhood deaths in urban Dhaka, Bangladesh

Data on causes of early childhood death from low-income urban areas are limited. The nationally representative Bangladesh Demographic and Health Survey 2007 estimates 65 children died per 1,000 live births. We investigated rates and causes of under-five deaths in an urban community near two large pediatric hospitals in Dhaka, Bangladesh and evaluated the impact of different recall periods. We conducted a survey in 2006 for 6971 households and a follow up survey in 2007 among eligible remaining households or replacement households. The initial survey collected information for all children under five years old who died in the previous year; the follow up survey on child deaths in the preceding five years. We compared mortality rates based on 1-year recall to the 4 years preceding the most recent 1 year. The initial survey identified 58 deaths among children <5 years in the preceding year. The follow up survey identified a mean 53 deaths per year in the preceding five years (SD±7.3). Under-five mortality rate was 34 and neonatal mortality was 15 per thousand live births during 2006–2007. The leading cause of under-five death was respiratory infections (22%). The mortality rates among children under 4 years old for the two time periods (most recent 1-year recall and the 4 years preceding the most recent 1 year) were similar (36 versus 32). The child mortality in urban Dhaka was substantially lower than the national rate. Mortality rates were not affected by recall periods between 1 and 5 years.

Recurrent zoonotic transmission of Nipah virus into humans, Bangladesh, 2001-2007

More than half of identified cases result from person-to-person transmission.

Human Nipah outbreaks recur in a specific region and time of year in Bangladesh. Fruit bats are the reservoir host for Nipah virus. We identified 23 introductions of Nipah virus into human populations in central and northwestern Bangladesh from 2001 through 2007. Ten introductions affected multiple persons (median 10). Illness onset occurred from December through May but not every year. We identified 122 cases of human Nipah infection. The mean age of case-patients was 27 years; 87 (71%) died. In 62 (51%) Nipah virus–infected patients, illness developed 5–15 days after close contact with another Nipah case-patient. Nine (7%) Nipah case-patients transmitted virus to others. Nipah case-patients who had difficulty breathing were more likely than those without respiratory difficulty to transmit Nipah (12% vs. 0%, p = 0.03). Although a small minority of infected patients transmit Nipah virus, more than half of identified cases result from person-to-person transmission. Interventions to prevent virus transmission from bats to humans and from person to person are needed.

The transmissibility and control of pandemic influenza A (H1N1) virus

Pandemic influenza A (H1N1) 2009 (pandemic H1N1) is spreading throughout the planet. It has become the dominant strain in the Southern Hemisphere, where the influenza season has now ended. Here, on the basis of reported case clusters in the United States, we estimated the household secondary attack rate for pandemic H1N1 to be 27.3% [95% confidence interval (CI) from 12.2% to 50.5%]. From a school outbreak, we estimated that a typical schoolchild infects 2.4 (95% CI from 1.8 to 3.2) other children within the school. We estimated the basic reproductive number, R0, to range from 1.3 to 1.7 and the generation interval to range from 2.6 to 3.2 days. We used a simulation model to evaluate the effectiveness of vaccination strategies in the United States for fall 2009. If a vaccine were available soon enough, vaccination of children, followed by adults, reaching 70% overall coverage, in addition to high-risk and essential workforce groups, could mitigate a severe epidemic.

Difficulties in maintaining improved handwashing behavior, Karachi, Pakistan

In an earlier study in Karachi, Pakistan, households that received free soap and handwashing promotion for 9 months reported 53% less diarrhea than controls. Eighteen months after the intervention ended, these households were enrolled in a follow-up study to assess sustainability of handwashing behavior. Upon re-enrollment, mothers in households originally assigned to the intervention were 1.5 times more likely to have a place with soap and water to wash hands (79% versus 53%, P = 0.001) and when asked to wash hands were 2.2 times more likely to rub their hands together at least three times (50% versus 23%, P = 0.002) compared with controls. In the ensuing 14 months, former intervention households reported a similar proportion of person-days with diarrhea (1.59% versus 1.88%, P = 0.66) as controls. Although intervention households showed better handwashing technique after 2 years without intervention, their soap purchases and diarrhea experience was not significantly different from controls.

Generation interval contraction and epidemic data analysis

The generation interval is the time between the infection time of an infected person and the infection time of his or her infector. Probability density functions for generation intervals have been an important input for epidemic models and epidemic data analysis. In this paper, we specify a general stochastic SIR epidemic model and prove that the mean generation interval decreases when susceptible persons are at risk of infectious contact from multiple sources. The intuition behind this is that when a susceptible person has multiple potential infectors, there is a "race" to infect him or her in which only the first infectious contact leads to infection. In an epidemic, the mean generation interval contracts as the prevalence of infection increases. We call this global competition among potential infectors. When there is rapid transmission within clusters of contacts, generation interval contraction can be caused by a high local prevalence of infection even when the global prevalence is low. We call this local competition among potential infectors. Using simulations, we illustrate both types of competition. Finally, we show that hazards of infectious contact can be used instead of generation intervals to estimate the time course of the effective reproductive number in an epidemic. This approach leads naturally to partial likelihoods for epidemic data that are very similar to those that arise in survival analysis, opening a promising avenue of methodological research in infectious disease epidemiology.

Network-based analysis of stochastic SIR epidemic models with random and proportionate mixing

In this paper, we outline the theory of epidemic percolation networks and their use in the analysis of stochastic susceptible-infectious-removed (SIR) epidemic models on undirected contact networks. We then show how the same theory can be used to analyze stochastic SIR models with random and proportionate mixing. The epidemic percolation networks for these models are purely directed because undirected edges disappear in the limit of a large population. In a series of simulations, we show that epidemic percolation networks accurately predict the mean outbreak size and probability and final size of an epidemic for a variety of epidemic models in homogeneous and heterogeneous populations. Finally, we show that epidemic percolation networks can be used to re-derive classical results from several different areas of infectious disease epidemiology. In an Appendix, we show that an epidemic percolation network can be defined for any time-homogeneous stochastic SIR model in a closed population and prove that the distribution of outbreak sizes given the infection of any given node in the SIR model is identical to the distribution of its out-component sizes in the corresponding probability space of epidemic percolation networks. We conclude that the theory of percolation on semi-directed networks provides a very general framework for the analysis of stochastic SIR models in closed populations.

Second look at the spread of epidemics on networks

In an important paper, Newman [Phys. Rev. E66, 016128 (2002)] claimed that a general network-based stochastic Susceptible-Infectious-Removed (SIR) epidemic model is isomorphic to a bond percolation model, where the bonds are the edges of the contact network and the bond occupation probability is equal to the marginal probability of transmission from an infected node to a susceptible neighbor. In this paper, we show that this isomorphism is incorrect and define a semidirected random network we call the epidemic percolation network that is exactly isomorphic to the SIR epidemic model in any finite population. In the limit of a large population, (i) the distribution of (self-limited) outbreak sizes is identical to the size distribution of (small) out-components, (ii) the epidemic threshold corresponds to the phase transition where a giant strongly connected component appears, (iii) the probability of a large epidemic is equal to the probability that an initial infection occurs in the giant in-component, and (iv) the relative final size of an epidemic is equal to the proportion of the network contained in the giant out-component. For the SIR model considered by Newman, we show that the epidemic percolation network predicts the same mean outbreak size below the epidemic threshold, the same epidemic threshold, and the same final size of an epidemic as the bond percolation model. However, the bond percolation model fails to predict the correct outbreak size distribution and probability of an epidemic when there is a nondegenerate infectious period distribution. We confirm our findings by comparing predictions from percolation networks and bond percolation models to the results of simulations. In the Appendix, we show that an isomorphism to an epidemic percolation network can be defined for any time-homogeneous stochastic SIR model.

Foodborne transmission of Nipah virus, Bangladesh

We investigated an outbreak of encephalitis in Tangail District, Bangladesh. We defined case-patients as persons from the outbreak area in whom fever developed with new onset of seizures or altered mental status from December 15, 2004, through January 31, 2005. Twelve persons met the definition; 11 (92%) died. Serum specimens were available from 3; 2 had immunoglobulin M antibodies against Nipah virus by capture enzyme immunoassay. We enrolled 11 case-patients and 33 neighborhood controls in a case-control study. The only exposure significantly associated with illness was drinking raw date palm sap (64% among case-patients vs. 18% among controls, odds ratio [OR] 7.9, p = 0.01). Fruit bats (Pteropus giganteus) are a nuisance to date palm sap collectors because the bats drink from the clay pots used to collect the sap at night. This investigation suggests that Nipah virus was transmitted from P. giganteus to persons through drinking fresh date palm sap.

Visceral leishmaniasis: consequences of a neglected disease in a Bangladeshi community

Visceral leishmaniasis, or kala azar (KA), affects the rural poor, causing significant morbidity and mortality. We examined the epidemiologic, social, and economic impact of KA in a village in Bangladesh. A population-based survey among 2,348 people demonstrated a KA incidence of 2% per year from 2000 to 2002, with a case-fatality rate of 19% among adult women, compared with 6-8% among other demographic groups. Kala azar cases were geographically clustered in certain sections of the village. Anti-leishmanial drug shortages and the high cost of diagnosis and treatment caused substantial emotional and economic hardship for affected families. Communities wanted to learn more about KA, and were willing to take collective action to confront the problems it causes. To decrease the KA burden in endemic areas, community efforts should be supplemented with effective treatment programs to ensure access to appropriate and affordable diagnosis and case management.