Deadly secret: situating the unknowing and knowing of the source of the Ebola epidemic in Northern Uganda

This article critically examines the unknowing of the source of the Ebola epidemic in Northern Uganda, in 2000/1, by asking how this unknowing has been achieved and has shaped the disease situation. Specifically, this article follows my informants’ explanation that soldiers of the Uganda People's Defence Force had brought the disease from the Democratic Republic of the Congo to Uganda. This account is widely rejected as a rumour by scientists, who insist that the source of the epidemic remains unknown. By contrast, I suggest that following these stories, as embodied experiences of the multiple connections between war and epidemics, human and nonhuman lives, provides crucial insights into the political ecology of Ebola in the wider region – a region where, even today, conflict and Ebola outbreaks are intricately interwoven. Understanding how unknowing is achieved and shapes a disease situation directs attention to forms of silent knowing, which illuminate what preparedness means in the political ecology of Ebola.     

How Ebola Impacts Genetics of Western Lowland Gorilla Populations

Background

Emerging infectious diseases in wildlife are major threats for both human health and biodiversity conservation. Infectious diseases can have serious consequences for the genetic diversity of populations, which could enhance the species'' extinction probability. The Ebola epizootic in western and central Africa induced more than 90% mortality in Western lowland gorilla population. Although mortality rates are very high, the impacts of Ebola on genetic diversity of Western lowland gorilla have never been assessed.

Methodology/Principal Findings

We carried out long term studies of three populations of Western lowland gorilla in the Republic of the Congo (Odzala-Kokoua National Park, Lossi gorilla sanctuary both affected by Ebola and Lossi''s periphery not affected). Using 17 microsatellite loci, we compared genetic diversity and structure of the populations and estimate their effective size before and after Ebola outbreaks. Despite the effective size decline in both populations, we did not detect loss in genetic diversity after the epizootic. We revealed temporal changes in allele frequencies in the smallest population.

Conclusions/Significance

Immigration and short time elapsed since outbreaks could explain the conservation of genetic diversity after the demographic crash. Temporal changes in allele frequencies could not be explained by genetic drift or random sampling. Immigration from genetically differentiated populations and a non random mortality induced by Ebola, i.e., selective pressure and cost of sociality, are alternative hypotheses. Understanding the influence of Ebola on gorilla genetic dynamics is of paramount importance for human health, primate evolution and conservation biology.     

Ebola Outbreak Response; Experience and Development of Screening Tools for Viral Haemorrhagic Fever (VHF) in a HIV Center of Excellence Near to VHF Epicentres

Introduction

There have been 3 outbreaks of viral hemorrhagic fever (VHF) in Uganda in the last 2 years. VHF often starts with non-specific symptoms prior to the onset of haemorrhagic signs. HIV clinics in VHF outbreak countries such as Uganda see large numbers of patients with HIV 1/2 infection presenting with non-specific symptoms every day. Whilst there are good screening tools for general health care facilities expecting VHF suspects, we were unable to find tools for use in HIV or other non-acute clinics.

Methods

We designed tools to help with communication to staff, infection control and screening of HIV patients with non-specific symptoms in a large HIV clinic during the outbreaks in Uganda. We describe our experiences in using these tools in 2 Ebola Virus Disease outbreaks in Uganda.

Results

During the Ebola Virus Disease (EVD) outbreaks, enhanced infection control and communication procedures were implemented within 24 hours of the WHO/Ministry of Health announcement of the outbreaks. During course of these outbreaks the clinic saw 12,544 patients with HIV 1/2 infection, of whom 3,713 attended without an appointment, suggesting new symptoms. Of these 4 were considered at risk of EVD and seen with full infection procedures; 3 were sent home after further investigation. One patient was referred to the National Referral Hospital VHF unit, but discharged on the same day. One additional VHF suspect was identified outside of a VHF outbreak; he was transferred to the National Referral Hospital and placed in isolation within 2 hours of arriving at the HIV clinic.

Discussion

Use of simple screening tools can be helpful in managing large numbers of symptomatic patients attending for routine and non-routine medical care (including HIV care) within a country experiencing a VHF outbreak, and can raise medical staff awareness of VHF outside of the epidemics.     

Stochastic eco-epidemiological model of dengue disease transmission by Aedes aegypti mosquito

We present a stochastic dynamical model for the transmission of dengue that takes into account seasonal and spatial dynamics of the vector Aedes aegypti. It describes disease dynamics triggered by the arrival of infected people in a city. We show that the probability of an epidemic outbreak depends on seasonal variation in temperature and on the availability of breeding sites. We also show that the arrival date of an infected human in a susceptible population dramatically affects the distribution of the final size of epidemics and that early outbreaks have a low probability. However, early outbreaks are likely to produce large epidemics because they have a longer time to evolve before the winter extinction of vectors. Our model could be used to estimate the risk and final size of epidemic outbreaks in regions with seasonal climatic variations.     

Transmission dynamics of the great influenza pandemic of 1918 in Geneva, Switzerland: Assessing the effects of hypothetical interventions

Recurrent outbreaks of the avian H5N1 influenza virus in Asia represent a constant global pandemic threat. We characterize and evaluate hypothetical public health measures during the 1918 influenza pandemic in the Canton of Geneva, Switzerland. The transmission rate, the recovery rate, the diagnostic rate, the relative infectiousness of asymptomatic cases, and the proportion of clinical cases are estimated through least-squares fitting of the model to epidemic curve data of the cumulative number of hospital notifications. The latent period and the case fatality proportion are taken from published literature. We determine the variance and identifiability of model parameters via a simulation study. Our epidemic model agrees well with the observed epidemic data. We estimate the basic reproductive number for the spring wave R1;=1.49 (95% CI: 1.45-1.53) and the reproductive number for the fall wave R2;=3.75 (95% CI: 3.57-3.93). In addition, we estimate the clinical reporting for these two waves to be 59.7% (95% CI: 55.7-63.7) and 83% (95% CI: 79-87). We surmise that the lower reporting in the first wave can be explained by a lack of initial awareness of the epidemic and the relative higher severity of the symptoms experienced during the fall wave. We found that effective isolation measures in hospital clinics at best would only ensure control with probability 0.87 while reducing the transmission rate by >76.5% guarantees stopping an epidemic.     

Optimal treatment of an SIR epidemic model with time delay

In this paper the optimal control strategies of an SIR (susceptible–infected–recovered) epidemic model with time delay are introduced. In order to do this, we consider an optimally controlled SIR epidemic model with time delay where a control means treatment for infectious hosts. We use optimal control approach to minimize the probability that the infected individuals spread and to maximize the total number of susceptible and recovered individuals. We first derive the basic reproduction number and investigate the dynamical behavior of the controlled SIR epidemic model. We also show the existence of an optimal control for the control system and present numerical simulations on real data regarding the course of Ebola virus in Congo. Our results indicate that a small contact rate(probability of infection) is suitable for eradication of the disease (Ebola virus) and this is one way of optimal treatment strategies for infectious hosts.     

Estimating the Basic Reproductive Number (R0) for African Swine Fever Virus (ASFV) Transmission between Pig Herds in Uganda

African swine fever (ASF) is a highly contagious, lethal and economically devastating haemorrhagic disease of domestic pigs. Insights into the dynamics and scale of virus transmission can be obtained from estimates of the basic reproduction number (R₀). We estimate R₀ for ASF virus in small holder, free-range pig production system in Gulu, Uganda. The estimation was based on data collected from outbreaks that affected 43 villages (out of the 289 villages with an overall pig population of 26,570) between April 2010 and November 2011. A total of 211 outbreaks met the criteria for inclusion in the study. Three methods were used, specifically; (i) GIS- based identification of the nearest infectious neighbour based on the Euclidean distance between outbreaks, (ii) epidemic doubling time, and (iii) a compartmental susceptible-infectious (SI) model. For implementation of the SI model, three approaches were used namely; curve fitting (CF), a linear regression model (LRM) and the SI/N proportion. The R₀ estimates from the nearest infectious neighbour and epidemic doubling time methods were 3.24 and 1.63 respectively. Estimates from the SI-based method were 1.58 for the CF approach, 1.90 for the LRM, and 1.77 for the SI/N proportion. Since all these values were above one, they predict the observed persistence of the virus in the population. We hypothesize that the observed variation in the estimates is a consequence of the data used. Higher resolution and temporally better defined data would likely reduce this variation. This is the first estimate of R₀ for ASFV in a free range smallholder pig keeping system in sub-Saharan Africa and highlights the requirement for more efficient application of available disease control measures.     

埃博拉病毒疫苗研究进展

埃博拉病毒是一种可引起人和非人灵长类动物出血热传染病的最为致命的烈性病毒,致死率可达90%。2014年在西非爆发的埃博拉疫情引起了全世界的关注。疫苗接种是预防和控制传染病最为常规和有效的方法,尽管目前还没有正式获得批准上市的埃博拉病毒疫苗,但是已有多个尚处于研究阶段的疫苗在非人灵长类动物上取得了很好的保护效果,并有几个已进入临床Ⅰ期试验阶段,有望尽快用于本次埃博拉疫情的防控。本文对目前处于研究阶段的多个类型的埃博拉病毒疫苗进行了综述,为相关研究人员提供参考。     

Interval estimates for epidemic thresholds in two-sex network models

Epidemic thresholds in network models of heterogeneous populations characterized by highly right-skewed contact distributions can be very small. When the population is above the threshold, an epidemic is inevitable and conventional control measures to reduce the transmissibility of a pathogen will fail to eradicate it. We consider a two-sex network model for a sexually transmitted disease which assumes random mixing conditional on the degree distribution. We derive expressions for the basic reproductive number (R(0)) for one and heterogeneous two-population in terms of characteristics of the degree distributions and transmissibility. We calculate interval estimates for the epidemic thresholds for stochastic process models in three human populations based on representative surveys of sexual behavior (Uganda, Sweden, USA). For Uganda and Sweden, the epidemic threshold is greater than zero with high confidence. For the USA, the interval includes zero. We discuss the implications of these findings along with the limitations of epidemic models which assume random mixing.     

Mapping of Ebola virus spillover: Suitability and seasonal variability at the landscape scale

The unexpected Ebola virus outbreak in West Africa in 2014 involving the Zaire ebolavirus made clear that other regions outside Central Africa, its previously documented niche, were at risk of future epidemics. The complex transmission cycle and a lack of epidemiological data make mapping areas at risk of the disease challenging. We used a Geographic Information System-based multicriteria evaluation (GIS-MCE), a knowledge-based approach, to identify areas suitable for Ebola virus spillover to humans in regions of Guinea, Congo and Gabon where Ebola viruses already emerged. We identified environmental, climatic and anthropogenic risk factors and potential hosts from a literature review. Geographical data layers, representing risk factors, were combined to produce suitability maps of Ebola virus spillover at the landscape scale. Our maps show high spatial and temporal variability in the suitability for Ebola virus spillover at a fine regional scale. Reported spillover events fell in areas of intermediate to high suitability in our maps, and a sensitivity analysis showed that the maps produced were robust. There are still important gaps in our knowledge about what factors are associated with the risk of Ebola virus spillover. As more information becomes available, maps produced using the GIS-MCE approach can be easily updated to improve surveillance and the prevention of future outbreaks.     

Potential for Ebola transmission between gorilla and chimpanzee social groups

Over the past decade Ebola hemorrhagic fever has emerged repeatedly in Gabon and Congo, causing numerous human outbreaks and massive die-offs of gorillas and chimpanzees. Why Ebola has emerged so explosively remains poorly understood. Previous studies have tended to focus on exogenous factors such as habitat disturbance and climate change as drivers of Ebola emergence while downplaying the contribution of transmission between gorilla or chimpanzee social groups. Here we report recent observations on behaviors that pose a risk of transmission among gorilla groups and between gorillas and chimpanzees. These observations support a reassessment of ape-to-ape transmission as an amplifier of Ebola outbreaks.     

Theory versus data: how to calculate R0?

To predict the potential severity of outbreaks of infectious diseases such as SARS, HIV, TB and smallpox, a summary parameter, the basic reproduction number R(0), is generally calculated from a population-level model. R(0) specifies the average number of secondary infections caused by one infected individual during his/her entire infectious period at the start of an outbreak. R(0) is used to assess the severity of the outbreak, as well as the strength of the medical and/or behavioral interventions necessary for control. Conventionally, it is assumed that if R(0)>1 the outbreak generates an epidemic, and if R(0)<1 the outbreak becomes extinct. Here, we use computational and analytical methods to calculate the average number of secondary infections and to show that it does not necessarily represent an epidemic threshold parameter (as it has been generally assumed). Previously we have constructed a new type of individual-level model (ILM) and linked it with a population-level model. Our ILM generates the same temporal incidence and prevalence patterns as the population-level model; we use our ILM to directly calculate the average number of secondary infections (i.e., R(0)). Surprisingly, we find that this value of R(0) calculated from the ILM is very different from the epidemic threshold calculated from the population-level model. This occurs because many different individual-level processes can generate the same incidence and prevalence patterns. We show that obtaining R(0) from empirical contact tracing data collected by epidemiologists and using this R(0) as a threshold parameter for a population-level model could produce extremely misleading estimates of the infectiousness of the pathogen, the severity of an outbreak, and the strength of the medical and/or behavioral interventions necessary for control.     

Modeling the Impact of Ebola and Bushmeat Hunting on Western Lowland Gorillas

The 2003 outbreak of Ebola in the Republic of Congo killed 114 people and up to 800 western lowland gorillas. This outbreak and all outbreaks between 2001–2003 began with human handling of infected animal carcasses. Ebola has since spread, putting the entire gorilla population at risk. An epidemiological model is presented to describe the combined effects of Ebola and hunting on persistence of gorillas. The number of infected gorillas also provides a means of assessing the risk of transmission to humans. Under current harvest practices and the estimated annual outbreak rate, the gorilla population is predicted to undergo a 97% decline within 100 years. Controlling bushmeat hunting may not be enough to prevent extinction if frequent outbreaks occur.     

Statistical inference in a stochastic epidemic SEIR model with control intervention: Ebola as a case study

A stochastic discrete-time susceptible-exposed-infectious-recovered (SEIR) model for infectious diseases is developed with the aim of estimating parameters from daily incidence and mortality time series for an outbreak of Ebola in the Democratic Republic of Congo in 1995. The incidence time series exhibit many low integers as well as zero counts requiring an intrinsically stochastic modeling approach. In order to capture the stochastic nature of the transitions between the compartmental populations in such a model we specify appropriate conditional binomial distributions. In addition, a relatively simple temporally varying transmission rate function is introduced that allows for the effect of control interventions. We develop Markov chain Monte Carlo methods for inference that are used to explore the posterior distribution of the parameters. The algorithm is further extended to integrate numerically over state variables of the model, which are unobserved. This provides a realistic stochastic model that can be used by epidemiologists to study the dynamics of the disease and the effect of control interventions.     

Evaluating surveillance strategies for the early detection of low pathogenicity avian influenza infections

In recent years, the early detection of low pathogenicity avian influenza (LPAI) viruses in poultry has become increasingly important, given their potential to mutate into highly pathogenic viruses. However, evaluations of LPAI surveillance have mainly focused on prevalence and not on the ability to act as an early warning system. We used a simulation model based on data from Italian LPAI epidemics in turkeys to evaluate different surveillance strategies in terms of their performance as early warning systems. The strategies differed in terms of sample size, sampling frequency, diagnostic tests, and whether or not active surveillance (i.e., routine laboratory testing of farms) was performed, and were also tested under different epidemiological scenarios. We compared surveillance strategies by simulating within-farm outbreaks. The output measures were the proportion of infected farms that are detected and the farm reproduction number (R(h)). The first one provides an indication of the sensitivity of the surveillance system to detect within-farm infections, whereas R(h) reflects the effectiveness of outbreak detection (i.e., if detection occurs soon enough to bring an epidemic under control). Increasing the sampling frequency was the most effective means of improving the timeliness of detection (i.e., it occurs earlier), whereas increasing the sample size increased the likelihood of detection. Surveillance was only effective in preventing an epidemic if actions were taken within two days of sampling. The strategies were not affected by the quality of the diagnostic test, although performing both serological and virological assays increased the sensitivity of active surveillance. Early detection of LPAI outbreaks in turkeys can be achieved by increasing the sampling frequency for active surveillance, though very frequent sampling may not be sustainable in the long term. We suggest that, when no LPAI virus is circulating yet and there is a low risk of virus introduction, a less frequent sampling approach might be admitted, provided that the surveillance is intensified as soon as the first outbreak is detected.     

Dynamics of COVID-19 under social distancing measures are driven by transmission network structure

In the absence of pharmaceutical interventions, social distancing is being used worldwide to curb the spread of COVID-19. The impact of these measures has been inconsistent, with some regions rapidly nearing disease elimination and others seeing delayed peaks or nearly flat epidemic curves. Here we build a stochastic epidemic model to examine the effects of COVID-19 clinical progression and transmission network structure on the outcomes of social distancing interventions. Our simulations show that long delays between the adoption of control measures and observed declines in cases, hospitalizations, and deaths occur in many scenarios. We find that the strength of within-household transmission is a critical determinant of success, governing the timing and size of the epidemic peak, the rate of decline, individual risks of infection, and the success of partial relaxation measures. The structure of residual external connections, driven by workforce participation and essential businesses, interacts to determine outcomes. We suggest limited conditions under which the formation of household “bubbles” can be safe. These findings can improve future predictions of the timescale and efficacy of interventions needed to control second waves of COVID-19 as well as other similar outbreaks, and highlight the need for better quantification and control of household transmission.     

Serologic cross-reactivity of human IgM and IgG antibodies to five species of Ebola virus

Five species of Ebola virus (EBOV) have been identified, with nucleotide differences of 30-45% between species. Four of these species have been shown to cause Ebola hemorrhagic fever (EHF) in humans and a fifth species (Reston ebolavirus) is capable of causing a similar disease in non-human primates. While examining potential serologic cross-reactivity between EBOV species is important for diagnostic assays as well as putative vaccines, the nature of cross-reactive antibodies following EBOV infection has not been thoroughly characterized. In order to examine cross-reactivity of human serologic responses to EBOV, we developed antigen preparations for all five EBOV species, and compared serologic responses by IgM capture and IgG enzyme-linked immunosorbent assay (ELISA) in groups of convalescent diagnostic sera from outbreaks in Kikwit, Democratic Republic of Congo (n=24), Gulu, Uganda (n=20), Bundibugyo, Uganda (n=33), and the Philippines (n=18), which represent outbreaks due to four different EBOV species. For groups of samples from Kikwit, Gulu, and Bundibugyo, some limited IgM cross-reactivity was noted between heterologous sera-antigen pairs, however, IgM responses were largely stronger against autologous antigen. In some instances IgG responses were higher to autologous antigen than heterologous antigen, however, in contrast to IgM responses, we observed strong cross-reactive IgG antibody responses to heterologous antigens among all sets of samples. Finally, we examined autologous IgM and IgG antibody levels, relative to time following EHF onset, and observed early peaking and declining IgM antibody levels (by 80 days) and early development and persistence of IgG antibodies among all samples, implying a consistent pattern of antibody kinetics, regardless of EBOV species. Our findings demonstrate limited cross-reactivity of IgM antibodies to EBOV, however, the stronger tendency for cross-reactive IgG antibody responses can largely circumvent limitations in the utility of heterologous antigen for diagnostic assays and may assist in the development of antibody-mediated vaccines to EBOV.     

Past and future epidemic potential of chikungunya virus in Australia

BackgroundAustralia is theoretically at risk of epidemic chikungunya virus (CHIKV) activity as the principal vectors are present on the mainland Aedes aegypti) and some islands of the Torres Strait (Ae. aegypti and Ae. albopictus). Both vectors are highly invasive and adapted to urban environments with a capacity to expand their distributions into south-east Queensland and other states in Australia. We sought to estimate the epidemic potential of CHIKV, which is not currently endemic in Australia, by considering exclusively transmission by the established vector in Australia, Ae. aegypti, due to the historical relevance and anthropophilic nature of the vector.Methodology/Principal findingsWe estimated the historical (1995–2019) epidemic potential of CHIKV in eleven Australian locations, including the Torres Strait, using a basic reproduction number equation. We found that the main urban centres of Northern Australia could sustain an epidemic of CHIKV. We then estimated future trends in epidemic potential for the main centres for the years 2020 to 2029. We also conducted uncertainty and sensitivity analyses on the variables comprising the basic reproduction number and found high sensitivity to mosquito population size, human population size, impact of vector control and human infectious period.Conclusions/SignificanceBy estimating the epidemic potential for CHIKV transmission on mainland Australia and the Torres Strait, we identified key areas of focus for controlling vector populations and reducing human exposure. As the epidemic potential of the virus is estimated to rise towards 2029, a greater focus on control and prevention measures should be implemented in at-risk locations.     

The Burden of Cholera in Uganda

Introduction

In 2010, the World Health Organization released a new cholera vaccine position paper, which recommended the use of cholera vaccines in high-risk endemic areas. However, there is a paucity of data on the burden of cholera in endemic countries. This article reviewed available cholera surveillance data from Uganda and assessed the sufficiency of these data to inform country-specific strategies for cholera vaccination.

Methods

The Uganda Ministry of Health conducts cholera surveillance to guide cholera outbreak control activities. This includes reporting the number of cases based on a standardized clinical definition plus systematic laboratory testing of stool samples from suspected cases at the outset and conclusion of outbreaks. This retrospective study analyzes available data by district and by age to estimate incidence rates. Since surveillance activities focus on more severe hospitalized cases and deaths, a sensitivity analysis was conducted to estimate the number of non-severe cases and unrecognized deaths that may not have been captured.

Results

Cholera affected all ages, but the geographic distribution of the disease was very heterogeneous in Uganda. We estimated that an average of about 11,000 cholera cases occurred in Uganda each year, which led to approximately 61–182 deaths. The majority of these cases (81%) occurred in a relatively small number of districts comprising just 24% of Uganda''s total population. These districts included rural areas bordering the Democratic Republic of Congo, South Sudan, and Kenya as well as the slums of Kampala city. When outbreaks occurred, the average duration was about 15 weeks with a range of 4–44 weeks.

Discussion

There is a clear subdivision between high-risk and low-risk districts in Uganda. Vaccination efforts should be focused on the high-risk population. However, enhanced or sentinel surveillance activities should be undertaken to better quantify the endemic disease burden and high-risk populations prior to introducing the vaccine.     

Evaluating Subcriticality during the Ebola Epidemic in West Africa

The 2014–2015 Ebola outbreak is the largest and most widespread to date. In order to estimate ongoing transmission in the affected countries, we estimated the weekly average number of secondary cases caused by one individual infected with Ebola throughout the infectious period for each affected West African country using a stochastic hidden Markov model fitted to case data from the World Health Organization. If the average number of infections caused by one Ebola infection is less than 1.0, the epidemic is subcritical and cannot sustain itself. The epidemics in Liberia and Sierra Leone have approached subcriticality at some point during the epidemic; the epidemic in Guinea is ongoing with no evidence that it is subcritical. Response efforts to control the epidemic should continue in order to eliminate Ebola cases in West Africa.