Variability in Intrahousehold Transmission of Ebola Virus, and Estimation of the Household Secondary Attack Rate

Individual and household risk factors for Ebola disease among household contacts in Mubende and Kassanda districts, Uganda, 2022

BACKGROUND

In 2022, an Ebola disease outbreak caused by Sudan virus (SUDV) occurred in Uganda, primarily affecting Mubende and Kassanda districts. We determined risk factors for SUDV infection among household members (HHM) of cases.

METHODS

We conducted a case-control and retrospective cohort study in January 2023. Cases were RT-PCR-confirmed SUDV infection in residents of Mubende or Kassanda districts during the outbreak. Case-households housed a symptomatic, primary case-patient for ≥ 24 h and had ≥ 1 secondary case-patient with onset < 2 weeks after their last exposure to the primary case-patient. Control households housed a case-patient and other HHM but no secondary cases. A risk factor questionnaire was administered to the primary case-patient or another adult who lived at home while the primary case-patient was ill. We conducted a retrospective cohort study among case-household members and categorized their interactions with primary case-patients during their illnesses as none, minimal, indirect, and direct contact. We conducted logistic regression to explore associations between exposures and case-household status, and Poisson regression to identify risk factors for SUDV infection among HHM.

RESULTS

Case- and control-households had similar median sizes. Among 19 case-households and 51 control households, primary case-patient death (adjusted odds ratio [ORadj] = 7.6, 95% CI 1.4-41) and ≥ 2 household bedrooms (ORadj=0.19, 95% CI 0.056-0.71) were associated with case-household status. In the cohort of 76 case-HHM, 44 (58%) were tested for SUDV < 2 weeks from their last contact with the primary case-patient; 29 (38%) were positive. Being aged ≥ 18 years (adjusted risk ratio [aRRadj] = 1.9, 95%CI: 1.01-3.7) and having direct or indirect contact with the primary case-patient (aRRadj=3.2, 95%CI: 1.1-9.7) compared to minimal or no contact increased risk of Sudan virus disease (SVD). Access to a handwashing facility decreased risk (aRRadj=0.52, 95%CI: 0.31-0.88).

CONCLUSION

Direct contact, particularly providing nursing care for and sharing sleeping space with SVD patients, increased infection risk among HHM. Risk assessments during contact tracing may provide evidence to justify closer monitoring of some HHM. Health messaging should highlight the risk of sharing sleeping spaces and providing nursing care for persons with Ebola disease symptoms and emphasize hand hygiene to aid early case identification and reduce transmission.

Effects of Mitigation and Control Policies in Realistic Epidemic Models Accounting for Household Transmission Dynamics

BACKGROUND

Compartmental infectious disease (ID) models are often used to evaluate nonpharmaceutical interventions (NPIs) and vaccines. Such models rarely separate within-household and community transmission, potentially introducing biases in situations in which multiple transmission routes exist. We formulated an approach that incorporates household structure into ID models, extending the work of House and Keeling.

DESIGN

We developed a multicompartment susceptible-exposed-infectious-recovered-susceptible-vaccinated (MC-SEIRSV) modeling framework, allowing nonexponentially distributed duration in exposed and infectious compartments, that tracks within-household and community transmission. We simulated epidemics that varied by community and household transmission rates, waning immunity rate, household size (3 or 5 members), and numbers of exposed and infectious compartments (1-3 each). We calibrated otherwise identical models without household structure to the early phase of each parameter combination's epidemic curve. We compared each model pair in terms of epidemic forecasts and predicted NPI and vaccine impacts on the timing and magnitude of the epidemic peak and its total size. Meta-analytic regressions characterized the relationship between household structure inclusion and the size and direction of biases.

RESULTS

Otherwise similar models with and without household structure produced equivalent early epidemic curves. However, forecasts from models without household structure were biased. Without intervention, they were upward biased on peak size and total epidemic size, with biases also depending on the number of exposed and infectious compartments. Model-estimated NPI effects of a 60% reduction in community contacts on peak time and size were systematically overestimated without household structure. Biases were smaller with a 20% reduction NPI. Because vaccination affected both community and household transmission, their biases were smaller.

CONCLUSIONS

ID models without household structure can produce biased outcomes in settings in which within-household and community transmission differ.

HIGHLIGHTS

Infectious disease models rarely separate household transmission from community transmission. The pace of household transmission may differ from community transmission, depends on household size, and can accelerate epidemic growth.Many infectious disease models assume exponential duration distributions for infected states. However, the duration of most infections is not exponentially distributed, and distributional choice alters modeled epidemic dynamics and intervention effectiveness.We propose a mathematical framework for household and community transmission that allows for nonexponential duration times and a suite of interventions and quantified the effect of accounting for household transmission by varying household size and duration distributions of infected states on modeled epidemic dynamics.Failure to include household structure induces biases in the modeled overall course of an epidemic and the effects of interventions delivered differentially in community settings. Epidemic dynamics are faster and more intense in populations with larger household sizes and for diseases with nonexponentially distributed infectious durations. Modelers should consider explicitly incorporating household structure to quantify the effects of non-pharmaceutical interventions (e.g., shelter-in-place).

Ebola virus disease: A review for the emergency medicine clinician

INTRODUCTION

Ebolavirus, the causative agent of Ebola virus disease (EVD) has been responsible for sporadic outbreaks mainly in sub-Saharan Africa since 1976. EVD is associated with high risk of transmission, especially to healthcare workers during patient care.

OBJECTIVE

The purpose of this review is to provide a concise review of EVD presentation, diagnosis, and management for emergency clinicians.

DISCUSSION

EVD is spread through direct contact, including blood, bodily fluids or contact with a contaminated object. Patients may present with non-specific symptoms such as fevers, myalgias, vomiting, or diarrhea that overlap with other viral illnesses, but rash, bruising, and bleeding may also occur. Laboratory analysis may reveal transaminitis, coagulopathy, and disseminated intravascular coagulation. The average clinical course is approximately 8-10 days with an average case fatality rate of 50%. The mainstay of treatment is supportive care, with two U.S. Food and Drug Administration-approved monoclonal antibody treatments (Ebanga and Inmazeb). Survivors of the disease may have a complicated recovery, marked by long-term symptoms.

CONCLUSION

EVD is a potentially deadly condition that can present with a wide range of signs and symptoms. Emergency clinicians must be aware of the presentation, evaluation, and management to optimize the care of these patients.

Risk factors for Ebola virus disease among household care providers, Sierra Leone, 2015

BACKGROUND

Household contacts who provide care to an Ebola virus disease (EVD) case have a 3-fold higher risk of EVD compared with contacts who do not provide care.

METHODS

We enrolled persons with confirmed EVD from December 2014 to April 2015 in Freetown, Sierra Leone, and their household contacts. Index cases and contacts were interviewed, and contacts were followed for 21 days to identify secondary cases. Epidemiological data were analysed to describe household care and to identify risk factors for developing EVD.

RESULTS

Of 838 contacts in 147 households, 156 (17%) self-reported providing care to the index case; 56 households had no care provider, 52 a single care provider and 39 multiple care providers. The median care provider age was 29 years, 68% were female and 32% were the index case's spouse. Care providers were more likely to report physical contact, contact with body fluids or sharing clothing, bed linens or utensils with an index case, compared with non-care providers (P <0.01). EVD risk among non-care providers was greater when the number of care providers in the household increased (odds ratio: 1.61; 95% confidence interval: 1.1, 2.4). In multivariable analysis, factors associated with care provider EVD risk included no piped water access and absence of index case fever, and protective factors included age <20 years and avoiding the index case.

CONCLUSIONS

Limiting the number of care providers in a household could reduce the risk of EVD transmission to both care providers and non-care providers. Strategies to protect care providers from EVD exposure are needed.

Effects of environmental variability on superspreading transmission events in stochastic epidemic models

Superspreaders (individuals with a high propensity for disease spread) have played a pivotal role in recent emerging and re-emerging diseases. In disease outbreak studies, host heterogeneity based on demographic (e.g. age, sex, vaccination status) and environmental (e.g. climate, urban/rural residence, clinics) factors are critical for the spread of infectious diseases, such as Ebola and Middle East Respiratory Syndrome (MERS). Transmission rates can vary as demographic and environmental factors are altered naturally or due to modified behaviors in response to the implementation of public health strategies. In this work, we develop stochastic models to explore the effects of demographic and environmental variability on human-to-human disease transmission rates among superspreaders in the case of Ebola and MERS. We show that the addition of environmental variability results in reduced probability of outbreak occurrence, however the severity of outbreaks that do occur increases. These observations have implications for public health strategies that aim to control environmental variables.

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.

The Emerging Threat of Ebola

Ebola is one of the deadliest infectious disease of the modern era. Over 50% of those infected die. Prior to 1976, the disease was unknown. No one knows exactly where it came from, but it is postulated that a mutation in an animal virus allowed it to jump species and infect humans. In 1976 simultaneous outbreaks of Ebola occurred in what is now South Sudan and the Democratic Republic of the Congo (DRC). For 20 years, only sporadic cases were seen, but in 1995 a new outbreak occurred killing hundreds in the DRC. Since that time the frequency of these outbreaks has been increasing. It is uncertain why this is occurring, but many associate it with increasing human encroachment into forested areas bringing people and animals into more intimate contact and increased mobility of previously remote population. This chapter will navigate Ebola in the context of global health and security.

There are multiple objectives of this chapter. First is to provide a basic understanding of Ebola disease processes and outbreak patterns. Second, is to explore the interplay between social determinants of health and Ebola. The role of technology in spreading Ebola outbreaks will be explained as will Ebola’s potential as a bioweapon. Readers will gain understanding of the link between environmental degradation and Ebola outbreaks.

This chapter will be divided into five main sections. These are (1) a case study; (2) Ebola Disease process; (3) Social determinants of health and Ebola; (4) Ebola in the modern era, and (5) the link between Ebola and environmental degradation.

Household Transmission of Ebola Virus: Risks and Preventive Factors, Freetown, Sierra Leone, 2015

Background

Knowing risk factors for household transmission of Ebola virus is important to guide preventive measures during Ebola outbreaks.

Methods

We enrolled all confirmed persons with Ebola who were the first case in a household, December 2014-April 2015, in Freetown, Sierra Leone, and their household contacts. Cases and contacts were interviewed, contacts followed prospectively through the 21-day incubation period, and secondary cases confirmed by laboratory testing.

Results

We enrolled 150 index Ebola cases and 838 contacts; 83 (9.9%) contacts developed Ebola during 21-day follow-up. In multivariable analysis, risk factors for transmission included index case death in the household, Ebola symptoms but no reported fever, age <20 years, more days with wet symptoms; and providing care to the index case (P < .01 for each). Protective factors included avoiding the index case after illness onset and a piped household drinking water source (P < .01 for each).

Conclusions

To reduce Ebola transmission, communities should rapidly identify and follow-up all household contacts; isolate those with Ebola symptoms, including those without reported fever; and consider closer monitoring of contacts who provided care to cases. Households could consider efforts to minimize risk by designating one care provider for ill persons with all others avoiding the suspected case.

Assessing Zika Virus Transmission Within Households During an Outbreak in Martinique, 2015-2016

Since 2015, Zika virus (ZIKV) has caused large epidemics in the Americas. Households are natural targets for control interventions, but quantification of the contribution of household transmission to overall spread is needed to guide policy. We developed a modeling framework to evaluate this contribution and key epidemic features of the ZIKV epidemic in Martinique in 2015-2016 from the joint analysis of a household transmission study (n = 68 households), a study among symptomatic pregnant women (n = 281), and seroprevalence surveys of blood donors (n = 457). We estimated that the probability of mosquito-mediated within-household transmission (from an infected member to a susceptible one) was 21% (95% credible interval (CrI): 5, 51), and the overall probability of infection from outside the household (i.e., in the community) was 39% (95% CrI: 27, 50). Overall, 50% (95% CrI: 43, 58) of the population was infected, with 22% (95% CrI: 5, 46) of infections acquired in households and 40% (95% CrI: 23, 56) being asymptomatic. The probability of presenting with Zika-like symptoms due to another cause was 16% (95% CrI: 10, 23). This study characterized the contribution of household transmission in ZIKV epidemics, demonstrating the benefits of integrating multiple data sets to gain more insight into epidemic dynamics.

Ebola Virus Disease in the Obstetric Population

The clinical management of Ebola created a significant challenge during the outbreak in West Africa, due to the paucity of previous research conducted into the optimum treatment regimen. That left many centres, to some extent, having to ‘work out’ best practice as they went along, and attempting to conduct real time prospective research. Médecins Sans Frontières (MSF) [1] were the only organization to have provided relatively in depth practical guidance prior to the outbreak and this manual was the basis of further planning between the WHO, national Ministry of Health and Sanitation in Sierra Leone, and other relevant stakeholders. Additionally, guidance changed over the epidemic as experience grew. This chapter will describe four key areas in the management of Ebola in West Africa. Firstly, it outlines the most recent WHO guidance; secondly, it looks back at how Ebola was managed in differing low and high resource settings; thirdly it outlines possible and optimal options for managing complications, paying particular attention to some of the controversies faced; fourthly it describes recent and ongoing studies into potential novel therapies that may shape future practice.

Surviving Ebola: A historical cohort study of Ebola mortality and survival in Sierra Leone 2014-2015

Background

While a number of predictors for Ebola mortality have been identified, less is known about post-viral symptoms. The identification of acute-illness predictors for post-viral symptoms could allow the selection of patients for more active follow up in the future, and those in whom early interventions may be beneficial in the long term. Studying predictors of both mortality and post-viral symptoms within a single cohort of patients could also further our understanding of the pathophysiology of survivor sequelae.

Methods/Principal findings

We performed a historical cohort study using data collected as part of routine clinical care from an Ebola Treatment Centre (ETC) in Kerry Town, Sierra Leone, in order to identify predictors of mortality and of post-viral symptoms. Variables included as potential predictors were sex, age, date of admission, first recorded viral load at the ETC and symptoms (recorded upon presentation at the ETC). Multivariable logistic regression was used to identify predictors. Of 263 Ebola-confirmed patients admitted between November 2014 and March 2015, 151 (57%) survived to ETC discharge. Viral load was the strongest predictor of mortality (adjusted OR comparing high with low viral load: 84.97, 95% CI 30.87–345.94). We did not find evidence that a high viral load predicted post-viral symptoms (ocular: 1.17, 95% CI 0.35–3.97; musculoskeletal: 1.07, 95% CI 0.28–4.08). Ocular post-viral symptoms were more common in females (2.31, 95% CI 0.98–5.43) and in those who had experienced hiccups during the acute phase (4.73, 95% CI 0.90–24.73).

Conclusions/Significance

These findings may add epidemiological support to the hypothesis that post-viral symptoms have an immune-mediated aspect and may not only be a consequence of high viral load and disease severity.