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Epidemic curve of Ebola virus disease cases: Guinea, Sierra Leone, Liberia, and 3-nation total by month, December 2013-March 2016.  

Epidemic curve of Ebola virus disease cases: Guinea, Sierra Leone, Liberia, and 3-nation total by month, December 2013-March 2016.  

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The 2013-2016 West Africa Ebola virus disease epidemic was notable for its scope, scale, and complexity. This briefing presents a series of distinguishing epidemiological features that set this outbreak apart. Compared to one concurrent and 23 previous outbreaks of the disease over 40 years, this was the only occurrence of Ebola virus disease invol...

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... Sexual transmission of EBOV is very rare but verified in several (continued) capping and containing the outbreak. 10,11 Case counts began a downward turn in the final months of 2014. In Liberia, EVD incidence dropped in November, while in Sierra Leone, the numbers of new cases remained high through December before falling steeply in January 2015 (Fig. 1). Total num- bers of new outbreak-associated cases decelerated rapidly throughout 2015. Starting from 1,800 new illnesses reported in January 2015, monthly counts dropped below 400 during June and finally reached the zero mark in all 3 intense transmission nations in the final months of the ...
Context 2
... 2013-2016 outbreak was the longest-duration EVD epidemic, continuing for almost 28 months, the time period from the date of diagnosis of the index case in December 2013 to the end of the PHEIC decla- ration. Within the 28-month span of the outbreak, the PHEIC was in force for 20 months, from August 8, 2014to March 29, 2016 As displayed in Fig. 1, the majority of EVD cases were diagnosed from August 2014 to February 2015. The time course varied across the 3 widespread trans- mission nations. Liberia displayed both the steepest rise in cases and the most rapid decline and became the first of the 3 nations to be declared "Ebola-free" on May 9, 2015. This declaration officially ...

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... During the past pandemic outbreaks, transportation of food supplies was always regarded as essential services. However, just as during the Ebola outbreak, people were afraid of going out to work in fear of contracting the disease (Shultz et al., 2016). This resulted in few people volunteering in transportation of food supplies during the food assistance programmes (Food and Agriculture Organization (FAO), 2018). ...
... Rice production across Central Africa uses labour-intensive methods involving teamwork in different stages of production mainly during planting and harvesting (Food and Agriculture Organization (FAO), 2018). A study by Shultz et al. (2016) revealed that due to fear of infection farmers were not willing join production teams leading to rice shortages across Central Africa. In Liberia, for example, 47% of farmers were not able to cultivate their land due to the outbreak of Ebola (Shultz et al., 2016). ...
... A study by Shultz et al. (2016) revealed that due to fear of infection farmers were not willing join production teams leading to rice shortages across Central Africa. In Liberia, for example, 47% of farmers were not able to cultivate their land due to the outbreak of Ebola (Shultz et al., 2016). However, the effects of Ebola on rice shortages did not spill over across the whole world as other major suppliers in China, India, and Southern Africa continued to feed the supply chains (Shultz et al., 2016). ...
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The COVID-19 pandemic took a devastating human and economic toll on the entire globe. Although Africa had fewer number of cases compared to all other continents, the influence of the COVID-19 pandemic on food supply chains was devastating, yet the pathways through which it affected the continent remain poorly documented and understood. With this background in mind, this study explored the ways in which food supply chains were disrupted by COVID-19 in Zimbabwe. The study used a qualitative methodology and a descriptive survey with 32 participants from food supply chain networks. The results reveal that due to COVID-19 restrictions, food supply chains were disrupted right from the production stage, transportation stage up to access markets. More specifically, COVID-19 restrictions negatively affected production and supply of food items. Secondly, transportation of food supplies was adversely affected as haulage companies as well as local suppliers and importers had challenges getting lockdown exemption letters. Thirdly, COVID-19 restrictions increased the demand for food stuffs as consumers stockpiled to ensure their food security. Lastly, COVID-19 pandemic increased food prices as consumer expenditure on food items increased. This study contributes to the body of knowledge since, to the best knowledge of the authors, it is the first study to identify and address the consequences of COVID-19 pandemic on food supply chains in Zimbabwe. This study is also significant to policy makers and practitioners in the optimization of food supply chains in possible subsequent waves and future pandemics. © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
... All the inferred imports correspond to isolates from 2015, despite most (544/1031) isolates in this collection being from 2014, which is statistically significant (Fisher's exact test comparing imported versus nonimported in 2014 versus 2015, P < 10 À4 ). This result coincides well with the incidence of Ebola over time in Sierra Leone and the two other badly affected neighbouring countries Guinea and Liberia (Shultz et al., 2016). The end of 2014 and the beginning of 2015 corresponds to the time when Sierra Leone managed to greatly reduce the number of Ebola cases, whereas other countries took longer to do so. ...
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Motivation: The ability to distinguish imported cases from locally acquired cases has important consequences for the selection of public health control strategies. Genomic data can be useful for this, for example using a phylogeographic analysis in which genomic data from multiple locations is compared to determine likely migration events between locations. However, these methods typically require good samples of genomes from all locations, which is rarely available. Results: Here we propose an alternative approach that only uses genomic data from a location of interest. By comparing each new case with previous cases from the same location we are able to detect imported cases, as they have a different genealogical distribution than that of locally acquired cases. We show that, when variations in the size of the local population are accounted for, our method has good sensitivity and excellent specificity for the detection of imports. We applied our method to data simulated under the structured coalescent model and demonstrate relatively good performance even when the local population has the same size as the external population. Finally, we applied our method to several recent genomic datasets from both bacterial and viral pathogens, and show that it can, in a matter of seconds or minutes, deliver important insights on the number of imports to a geographically limited sample of a pathogen population. Availability and implementation: The R package DetectImports is freely available from https://github.com/xavierdidelot/DetectImports. Supplementary information: Supplementary data are available at Bioinformatics online.
... TIM-1 serves as the receptor for Ebola virus in vivo (Brunton et al., 2019). Ebola infection occurs when the virus gains access to a "susceptible host" via a "portal of entry" (skin or mucous membranes) (Shultz, 2016). Susceptibility and permissiveness are two biological properties of virus-host interactions, with a biothermodynamic background. ...
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... The large spectrum of COVID-19 symptoms has fuelled misperceptions about the disease setting, development, and outcome. Considering that the COVID-19 cases-to-fatalities ratio is relatively low compared with the Ebola virus (40% during the 2013−2016 outbreak), 7 some governments quickly relaxed lockdowns to allow the reopening of businesses and services to avoid the collapse of their economies. In some cases, this permissiveness promoted massive infection to achieve herd immunity before vaccination. ...
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Background Brazil has been severely impacted by COVID-19 pandemics that is aggravated by the absence of a scientifically-driven coordinated informative campaign and the interference in public health management, which ultimately affected health measures to avoid SARS-CoV2 spread. The decentralization and resultant conflicts in disease control activities produced different protection behaviours and local government measures. In the present study, we investigated how political partisanship and socio-economic factors determined the outcome of COVID-19 at the local level in Brazil. Methods A retrospective study of COVID-19 deaths was carried out using mortality databases between Feb 2020, and Jun 2021 for the 5570 Brazilian municipalities. Socio-economic parameters including city categories, income and inequality indexes, health service quality and partisanship, assessed by the result of the second round of the 2018 Brazilian presidential elections, were included. Regression tree analysis was carried out to identify the statistical significance and conditioning relationships of variables. Findings Municipalities that supported then-candidate Jair Bolsonaro in the 2018 elections were those that had the worst COVID-19 mortality rates, mainly during the second epidemic wave of 2021. This pattern was observed even considering structural inequalities among cities. Interpretation In general, the first phase of the pandemic hit large and central cities hardest, while the second wave mostly impacted Bolsonarian municipalities, where scientific denialism among the population was stronger. Negative effects of partisanship towards the right-wing on COVID-19 outcomes counterbalances favourable socioeconomic indexes in affluent Brazilian cities. Our results underscore the fragility of public health policies which were undermined by the scientific denialism of right-wing supporters in Brazil. Funding International joint laboratories of Institute de Recherche pour le Développement, a partnership between the University of Brasília and the Oswaldo Cruz Foundation (LMI-Sentinela - UnB - Fiocruz - IRD), Coordination for the Improvement of Higher Education Personnel (CAPES), National Council for Scientific and Technological Development (CNPq).
... Notably, the largest impact on education was experienced during the Ebola virus disease (EVD) outbreak in West Africa between the years 2014 and 2016 [25,26]. During this outbreak, the countries that were most exposed to the virus were Guinea, Liberia, and Sierra Leone, with 28,610 cases and 11,308 deaths documented [25]. ...
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Secondary education is the second stage of formal education and traditionally begins after primary school, usually about age 11 to 13. The COVID-19 pandemic caused immeasurable changes to the educational system which inevitably greatly impacted secondary education. The current entry describes the changes in secondary education imposed by the pandemic and explores the accompanying challenges.
... In comparison, during the SARS epidemic, those who worked in healthcare facilities were considered much more likely to become infected with SARS than those in the general population, which led to considerable stigmatization of HCWs (Goulia et al., 2010;Hsin & Macer, 2004;Styra et al., 2008). Another factor that could have led to limited stigmatization of HCWs during the H1N1 pandemic, may have been the relatively low mortality rate associated with the virus in comparison to the other reviewed IDOs (Petersen et al., 2020;Shultz et al., 2016), leading some to perceive H1N1 as a less severe illness (Corley et al., 2010;Petersen et al., 2020). Interestingly, the estimated upper limit of the global death toll from H1N1 far exceeded that of SARS, MERS, and EVD combined (Petersen et al., 2020;Shultz et al., 2016), suggesting that the stigmatization of HCWs may be more correlated with the mortality rate of a specific IDO than the total number of deaths. ...
... Another factor that could have led to limited stigmatization of HCWs during the H1N1 pandemic, may have been the relatively low mortality rate associated with the virus in comparison to the other reviewed IDOs (Petersen et al., 2020;Shultz et al., 2016), leading some to perceive H1N1 as a less severe illness (Corley et al., 2010;Petersen et al., 2020). Interestingly, the estimated upper limit of the global death toll from H1N1 far exceeded that of SARS, MERS, and EVD combined (Petersen et al., 2020;Shultz et al., 2016), suggesting that the stigmatization of HCWs may be more correlated with the mortality rate of a specific IDO than the total number of deaths. ...
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Objective To synthesize qualitative literature exploring the lived experience of healthcare workers (HCWs) who cared for patients during the following infectious disease outbreaks (IDOs): the 2003 SARS epidemic, 2009 H1N1 pandemic, 2012 MERS outbreak, and 2014 EVD epidemic. We aim to reveal the collective experience of HCWs during these four IDOs and to create a reference for comparison of current and future IDOs. Methods Three electronic databases were searched, yielding 823 results after duplicates were removed. Forty qualitative and mixed-methods studies met the criteria for full file review. Fourteen studies met the inclusion and exclusion criteria. The data from the Results or Findings sections were manually coded and themes were conceptualized using thematic analysis. Results Of the 14 studies, 28.6% focused on SARS, 21.4% on H1N1, 21.4% on MERS, and 28.6% on EVD. Studies occurred in six different countries and included physicians, nurses, paramedics, and emergency medical technicians as participants. Five themes were conceptualized: Uncertainty, Adapting to Change, Commitment, Sacrifice, and Resilience. Conclusion This review identified the collective experience of HCWs caring for patients during four 21st century IDOs. This qualitative systematic review offers a reference to compare similarities and differences of other IDOs, including the COVID-19 pandemic.
... Many, especially women with disabilities, sought help from traditional healers, or attempted to self-treat themselves and their families. There were also significant fears about entering Ebola treatment units (ETU) during the outbreak, as well as fears about stigma and exclusion [27]. Moreover, if basic services like maternal and child health and malaria interventions were so significantly reduced, it can be assumed that the minimal services and supports previously available for people with disabilities (including those run by international NGOs who left the country) were significantly reduced or eliminated in face of Ebola. ...
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Background There has been little research on the impact of the 2014-2015 West African Ebola crisis on people with disabilities. This paper outlines the way in which the Ebola Virus Disease (EVD) outbreak in Liberia in 2015 highlighted existing inequalities and exclusion of people with disabilities and their households. Methods The results presented here are part of a larger ESRC/DFID-funded mixed methods research project in Liberia (2014-2017) which included a quantitative household survey undertaken in five counties, complemented by qualitative focus group discussions and interviews with people with disabilities and other key stakeholders. Uniquely, this research gathered information about people with disabilities’ experience of the EVD outbreak, as well as additional socioeconomic and inclusion data, that compared their experience with non-disabled community members. Results Reflections by people with disabilities themselves show knowledge, preparation, and responses to the EVD epidemic was often markedly different among people with disabilities due to limited resources, lack of inclusion by many mainstream public health and medical interventions and pre-existing discrimination, marginalisation and exclusion. Interviews with other key stakeholder revealed a lack of awareness of disability issues or sufficient training to include this population systematically in both Ebola response activities and general health services. Key findings include the need to understand and mitigate direct and indirect health consequences of unequal responses to the epidemic, as well as the limited capacity of healthcare and social services to respond to people with disabilities. Conclusion There are lessons to be learned from Ebola outbreak around inclusion of people with disabilities, relevant to the current COVID-19 pandemic. Now is the time to undertake measures to ensure that people with disabilities do not continue to be marginalised and excluded during global public health emergencies.
... Healthcare providers (HCPs) are usually at a great risk of contracting the infections -especially in the case of EVD, as it is a virus with a high potential for humanto-human transmission and a high fatality rate There is no doubt that EVD represents a major occupational risk to all HCPs who come into contact with infected patients. During the 2014 outbreak of EVD, 881 healthcare workers became infected with the disease in Guinea, Liberia, and Sierra Leone, and 513 of them lost their lives (case fatality rate: 58.2%) [5]. In Sierra Leone, where more HCPs were infected and died than in any other country, the confirmed EVD incidence in healthcare workers was 100-fold higher than that of the adult general population [6 , 7]. ...
... The questionnaire was composed of nine sections, collecting the respondent's (1) background data, (2) general knowledge and attitude about VHFs, (3) knowledge of the epidemiology of VHFs in Sudan, (4) adherence to guidelines regarding VHFs, (5,6) general knowledge about EVD and its nature, (7) knowledge about the prevention of EVD, (8) willingness to manage and deal with cases of EVD, and (9) preparedness for any potential viral outbreak. ...
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Ebola virus disease (EVD) is a highly contagious and fatal disease in humans. Healthcare providers (HCPs) are often at the frontline of epidemics and can thus be in jeopardy of contracting EVD. Sudan is at a great risk of an EVD outbreak, as it borders countries that experienced EVD outbreaks. It is therefore imperative in Sudan to assess the HCPs' awareness and knowledge, attitude, and practice (KAP) about EVD for its control and man-agement and for preparedness. A KAP survey was conducted among 387 HCPs (physicians, nurses and labora-tory technicians) in the three main tertiary hospitals in Khartoum, Sudan. The majority of the survey respon-dents (54.5%) were females, < 30 years old (76.3%), and single (77.4%). Most (94%) had heard about EVD, 62% from classical media. Only 14% had received education or training regarding EVD. About 40% reported being adherent to universal precautions and 72% were willing to deal with EVD patients under safety precau-tions. Only 10% knew of any available standard national guidelines for EVD. Nearly half of the HCPs (47%) rated the potential risk of an EVD outbreak in Sudan as high, and 52% rated health authorities' effort against it as weak. These findings revealed the HCPs' insufficient knowledge of EVD and the necessary universal precau-tions. This lack of knowledge would negatively affect the HCPs' preparedness toward any potential EVD out-break. There is a dire need to train HCPs in Sudan on the management of EVD, including preventive and con-trol measures.
... One is called Patient Information Based Algorithm (PIBA). It will predict death rates in the near MERS-CoV 34% [25] Seasonal flu (US) 0.1 to 0.2% [26] Ebola 50%, 40% in the 2013-16 outbreak [27,28] future based on real-time data, meaning that it uses patients' data in the early stages of the disease to count the average number of being hospitalized until death. PIBA calculation uses a number of deaths per a given day and divide it by the number of possible patients on the same day the patients have just showed symptoms of the disease. ...
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Numerical data on fifty pioneer adopting countries of Covid-19 novel pandemic has been put into study each on their first month of the virus’s spread. Ten groups were created with each including the countries of around the same average population density. This was meant to study the difference between each group’s growth of infected cases in addition to their mortality percentage rates (MPR). Modeling was done for each group with the use of MATLAB; for the growth rate, the exponential growth equation was considered, and for the mortality rate percentage, the equation was modeled over the span of the first chosen thirty days of the virus’s spread. An analysis was done with the consideration of Oxford’s Stringency Index Model (SIM) of government responses for each day in order to understand the effect of such taken regulations in the virus’s spread and fatality for different groups of population densities. Eventually, it was found that population density is not a significant factor in the virus’s infectiousness and mortality percentage rates; Instead, taking governmental responses of appropriate stringencies is found to be the key solution for control and protection.
... 3 While nearly all Ebola outbreaks have come to be classified as epidemics, the 2013-2016 crisis is understood as a pandemic. Although the majority of cases were concentrated in three countries in West Africa (Guinea, Sierra Leone and Liberia), associated cases were identified across 7 other countries on 3 continents (Shultz et al. 2016). According to The Dictionary of Epidemiology, an epidemic is defined as "the occurrence in a community or region of cases of an illness, specific health-related behaviour, or other health-related events clearly in excess of normal expectancy" (Porta 2014). ...
... The regional intensity and length of closures during the 2013-2016 Ebola pandemic in West Africa make it the only education system shutdown that comes close to what the world is currently experiencing. This pandemic had more cases, deaths and recoveries than all other prior Ebola outbreaks combined (Shultz et al. 2016). Previously, Ebola had never crossed national boundaries; the 2013-2016 outbreak was the first to be recognised as a pandemic, with reported cases spread across 10 countries. ...
... Previously, Ebola had never crossed national boundaries; the 2013-2016 outbreak was the first to be recognised as a pandemic, with reported cases spread across 10 countries. Sierra Leone was the epicentre of the outbreak, with all districts in the country reporting at least one case of Ebola (Amara et al. 2017), and with 99.9% of all cases concentrated in Guinea, Liberia and Sierra Leone (Shultz et al. 2016). Schools in these three countries were closed for seven (Guinea) to nine (Sierra Leone) months, resulting in 486 to 780 lost learning hours (UNDP 2015). ...
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The COVID-19 pandemic has seen an unprecedented shutdown of society. Among the various safety measures taken, much attention has been given to school closure as a non-pharmaceutical mitigation tool to curb the spread of the disease through ensuring “social” (physical) distancing. Nearly 1.725 billion children in over 95% of countries worldwide have been affected by school closures implemented in April 2020 as the virus continued to spread. In the field of education, policymakers’ attention has been directed at keeping students on board through remote learning and addressing the immediate needs of schools upon reopening. The study presented in this article focuses on who remains absent after schools resume. Using publicly available survey data from the USAID Demographic Health Surveys Program and the UNICEF Multiple Indicator Cluster Survey from before and after the 2013–2016 Ebola pandemic in Guinea and Sierra Leone in West Africa, the author examined changes in school enrolment and dropout patterns, with targeted consideration given to traditionally marginalised groups. At the time, schools closed for between seven to nine months in the two countries; this length and intensity makes this Ebola pandemic the only health crisis in the recent past to come close to the pandemic-related school closures experienced in 2020. The author’s findings suggest that post-Ebola, youth in the poorest households saw the largest increase in school dropout. Exceeding expected pre-Ebola dropout rates, an additional 17,400 of the poorest secondary-age youth were out of school. This evidence is important for minimising the likely post-COVID-19 expansion in inequality. The author’s findings point to the need for sustainable planning that looks beyond the reopening of educational institutions to include comprehensive financial support packages for groups most likely to be affected.