Characterizing risk of Ebola transmission based on frequency and type of case-contact exposures

Ambulance dispatching during a pandemic: Tradeoffs of categorizing patients and allocating ambulances

Amidst a pandemic, operators of emergency medical service (EMS) systems aim at upholding service at sufficiently low response times while reducing the infection probability of their personnel. Designating ambulances to serve only infected patients and suspected cases may reduce the outage probabilities of ambulances and consequently the response times of the EMS. We investigate the benefits that EMS personnel and patients can gain from such a split. As a solution method to quantify these benefits, we apply a two-stage approach. First, we run a first-stage optimization model to pre-select ambulance splits with the highest emergency call coverage. Second, we solve the approximate Hypercube Queuing Model (AHQM) to evaluate the performance of the pre-selected ambulance splits at the second stage. We contribute to the existing literature by including multiple incident categories and outages of ambulances in the AHQM and combining it with the first-stage optimization model. Further, we conduct a case study for the Coronavirus Disease 2019 (Covid-19) pandemic to draw conclusions on the benefits of splitting. We observe that an ambulance split would not reduce the average response time for the examined data set since the Covid-related call volume in Munich and the infection probability are too low. However, a sensitivity analysis shows that long isolation times and high infection probabilities make an ambulance split beneficial for patients and EMS personnel, as an ambulance split reduces the average response time without significantly increasing the mean infection probability for EMS personnel.

Homecare for sick family members while waiting for medical help during the 2014-2015 Ebola outbreak in Sierra Leone: a mixed methods study

INTRODUCTION

Caring for an Ebola patient is a known risk factor for disease transmission. In Sierra Leone during the outbreak in 2014/2015, isolation of patients in specialised facilities was not always immediately available and caring for a relative at home was sometimes the only alternative. This study sought to assess population-level protective caregiving intentions, to understand how families cared for their sick and to explore perceived barriers and facilitators influencing caregiving behaviours.

METHODS

Data from a nationwide household survey conducted in December 2014 were used to assess intended protective behaviours if caring for a family member with suspected Ebola. Their association with socio-demographic variables, Ebola-specific knowledge and risk perception was analysed using multilevel logistic regression. To put the results into context, semi-structured interviews with caregivers were conducted in Freetown.

RESULTS

Ebola-specific knowledge was positively associated with the intention to avoid touching a sick person and their bodily fluids (adjusted OR (AOR) 1.29; 95% CI 1.01 to 1.54) and the intention to take multiple protective measures (AOR 1.38; 95% CI 1.16 to 1.63). Compared with residing in the mostly urban Western Area, respondents from the initial epicentre of the outbreak (Eastern Province) had increased odds to avoid touching a sick person or their body fluids (AOR 4.74; 95% CI 2.55 to 8.81) and to take more than one protective measure (AOR 2.94; 95% CI 1.37 to 6.34). However, interviews revealed that caregivers, who were mostly aware of the risk of transmission and general protective measures, felt constrained by different contextual factors. Withholding care was not seen as an option and there was a perceived lack of practical advice.

CONCLUSIONS

Ebola outbreak responses need to take the sociocultural reality of caregiving and the availability of resources into account, offering adapted and acceptable practical advice. The necessity to care for a loved one when no alternatives exist should not be underestimated.

A Mathematical Model of Contact Tracing during the 2014–2016 West African Ebola Outbreak

The 2014–2016 West African outbreak of Ebola Virus Disease (EVD) was the largest and most deadly to date. Contact tracing, following up those who may have been infected through contact with an infected individual to prevent secondary spread, plays a vital role in controlling such outbreaks. Our aim in this work was to mechanistically represent the contact tracing process to illustrate potential areas of improvement in managing contact tracing efforts. We also explored the role contact tracing played in eventually ending the outbreak. We present a system of ordinary differential equations to model contact tracing in Sierra Leonne during the outbreak. Using data on cumulative cases and deaths, we estimate most of the parameters in our model. We include the novel features of counting the total number of people being traced and tying this directly to the number of tracers doing this work. Our work highlights the importance of incorporating changing behavior into one’s model as needed when indicated by the data and reported trends. Our results show that a larger contact tracing program would have reduced the death toll of the outbreak. Counting the total number of people being traced and including changes in behavior in our model led to better understanding of disease management.

Ebola Virus Dose Response Model for Aerosolized Exposures: Insights from Primate Data

This study develops dose-response models for Ebolavirus using previously published data sets from the open literature. Two such articles were identified in which three different species of nonhuman primates were challenged by aerosolized Ebolavirus in order to study pathology and clinical disease progression. Dose groups were combined and pooled across each study in order to facilitate modeling. The endpoint of each experiment was death. The exponential and exact beta-Poisson models were fit to the data using maximum likelihood estimation. The exact beta-Poisson was deemed the recommended model because it more closely approximated the probability of response at low doses though both models provided a good fit. Although transmission is generally considered to be dominated by person-to-person contact, aerosolization is a possible route of exposure. If possible, this route of exposure could be particularly concerning for persons in occupational roles managing contaminated liquid wastes from patients being treated for Ebola infection and the wastewater community responsible for disinfection. Therefore, this study produces a necessary mathematical relationship between exposure dose and risk of death for the inhalation route of exposure that can support quantitative microbial risk assessment aimed at informing risk mitigation strategies including personal protection policies against occupational exposures.

nosoi: A stochastic agent-based transmission chain simulation framework in r

The transmission process of an infectious agent creates a connected chain of hosts linked by transmission events, known as a transmission chain. Reconstructing transmission chains remains a challenging endeavour, except in rare cases characterized by intense surveillance and epidemiological inquiry. Inference frameworks attempt to estimate or approximate these transmission chains but the accuracy and validity of such methods generally lack formal assessment on datasets for which the actual transmission chain was observed.We here introduce nosoi, an open-source r package that offers a complete, tunable and expandable agent-based framework to simulate transmission chains under a wide range of epidemiological scenarios for single-host and dual-host epidemics. nosoi is accessible through GitHub and CRAN, and is accompanied by extensive documentation, providing help and practical examples to assist users in setting up their own simulations.Once infected, each host or agent can undergo a series of events during each time step, such as moving (between locations) or transmitting the infection, all of these being driven by user-specified rules or data, such as travel patterns between locations. nosoi is able to generate a multitude of epidemic scenarios, that can-for example-be used to validate a wide range of reconstruction methods, including epidemic modelling and phylodynamic analyses. nosoi also offers a comprehensive framework to leverage empirically acquired data, allowing the user to explore how variations in parameters can affect epidemic potential. Aside from research questions, nosoi can provide lecturers with a complete teaching tool to offer students a hands-on exploration of the dynamics of epidemiological processes and the factors that impact it. Because the package does not rely on mathematical formalism but uses a more intuitive algorithmic approach, even extensive changes of the entire model can be easily and quickly implemented.

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.

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

Transmission between family members accounts for most Ebola virus transmission, but little is known about determinants of intrahousehold spread. From detailed exposure histories, intrahousehold transmission chains were created for 94 households of Ebola survivors in Sierra Leone: 109 (co-)primary cases gave rise to 317 subsequent cases (0-100% of those exposed). Larger households were more likely to have subsequent cases, and the proportion of household members affected depended on individual and household-level factors. More transmissions occurred from older than from younger cases, and from those with more severe disease. The estimated household secondary attack rate was 18%.

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.

Epidemiological characteristics, clinical manifestations, and treatment outcome of 139 paediatric Ebola patients treated at a Sierra Leone Ebola treatment center

Background

The West Africa Ebola Virus Disease (EVD) outbreak in 2014–2016 was declared by the World Health Organization (WHO) a public health emergency of international concern. Most of the previous studies done in Sierra Leone relating to the clinical and epidemiological features of EVD during the 2014–2016 West African outbreak focused on adult EVD patients. There have been conflicting reports about the effects of EVD on children during previous outbreaks.

Methods

This is an observational retrospective analysis of medical data of all laboratory confirmed paediatric EVD patients below 15 years of age who were admitted at the 34 Military Hospital Ebola Treatment Center (ETC) in Wilberforce, Sierra Leone between June 2014 to April 2015. We analyzed the sociodemographic and clinical characteristics of paediatric EVD cases contained in case report forms that were collected by Ebola surveillance officers and clinicians at the 34 Military Hospital ETC. Both univariate and multivariate logistic regression models were used to determine the sociodemographic and clinical characteristics of paediatric EVD patients that were associated with EVD facility-based mortality.

Results

The majority of the paediatric EVD cases in this study were female (56.1%), pupils (51.1%), and 43.2% belonged to the age group between 10 years and below 15 years. The median age of the paediatric EVD cases was 9 years (interquartile range = 4 to 11 years). Adjusting for other covariates in the model, male paediatric EVD patient (AOR = 13.4, 95% CI = [2.07–156-18], p <  0.05), EVD patient with abdominal pain (AOR = 11.0, 95% CI = [1.30–161.81], p <  0.05), vomiting (AOR = 35.7, 95% CI = [3.43–833.73], p <  0.05), signs of conjunctivitis (AOR = 17.4, 95% CI = [1.53–342.21], p <  0.05) and difficulty in breathing (AOR = 23.3, 95% CI = [1.92–713.01], p <  0.05) at the time of admission had increased odds of dying during EVD treatment.

Conclusions

We recommend the adoption of case definitions currently in vigour to cater for specific characteristics of paediatric patients. Subgroups that can be identified by applying the model developed in this study may require special attention and intensified care.

Herd Immunity to Ebolaviruses Is Not a Realistic Target for Current Vaccination Strategies

The recent West African Ebola virus pandemic, which affected >28,000 individuals increased interest in anti-Ebolavirus vaccination programs. Here, we systematically analyzed the requirements for a prophylactic vaccination program based on the basic reproductive number (R₀, i.e., the number of secondary cases that result from an individual infection). Published R₀ values were determined by systematic literature research and ranged from 0.37 to 20. R₀s ≥ 4 realistically reflected the critical early outbreak phases and superspreading events. Based on the R₀, the herd immunity threshold (I_c) was calculated using the equation I_c = 1 - (1/R₀). The critical vaccination coverage (V_c) needed to provide herd immunity was determined by including the vaccine effectiveness (E) using the equation V_c = I_c/E. At an R₀ of 4, the I_c is 75% and at an E of 90%, more than 80% of a population need to be vaccinated to establish herd immunity. Such vaccination rates are currently unrealistic because of resistance against vaccinations, financial/logistical challenges, and a lack of vaccines that provide long-term protection against all human-pathogenic Ebolaviruses. Hence, outbreak management will for the foreseeable future depend on surveillance and case isolation. Clinical vaccine candidates are only available for Ebola viruses. Their use will need to be focused on health-care workers, potentially in combination with ring vaccination approaches.