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This chapter illustrates the implications of river network structure for the spread of waterborne diseases. Human mobility is also added as a driver and a network of interaction. The water-related (WR) diseases considered are epidemic cholera, endemic schistosomiasis, and proliferative kidney disease in fish. After reviewing the basic (space-implicit) epidemiological models for micro- and micro-parasites, general space-explicit models for both kinds of parasites are studied. Both the hydrologic and the human mobility network are included, and the general conditions for disease establishment are derived, including also the case of seasonal forcings. Conditions for transient (though possibly large) epidemics are also found. The microparasitic model is applied to several cholera epidemics, including the one that has been devastating Haiti. Spatially explicit macroparasitic models of schistosomiasis are then analyzed and applied to the cases of Senegal and Burkina Faso. Proliferative kidney disease (PKD) in salmonid fish, a pathology linked to global warming, is modeled. The space-explicit approach is used for the study of PKD spread in the basin of the river Wigger (Switzerland).
To determine the association between food insecurity and HIV-infection with depression and anxiety among new tuberculosis patients.
Our cross-sectional study assessed depression, anxiety, and food insecurity with Patient Health Questionnaire (PHQ9), Zung Anxiety Self-Assessment Scale (ZUNG), and Household Food Insecurity Access Scale, respectively. Poisson regression models with robust variance were used to examine correlates of depression (PHQ9 ≥ 10) and anxiety (ZUNG ≥ 36).
Patients who were newly diagnosed with tuberculosis.
Between January and December 2019, we enrolled 180 TB patients from primary health clinics in Botswana. Overall, 99 (55.0%) were HIV-positive, 47 (26.1%), 85 (47.2%), and 69 (38.5%) indicated depression, anxiety, and moderate to severe food insecurity, respectively. After adjusting for potential confounders, food insecurity was associated with a higher prevalence of depression (adjusted prevalence ratio [aPR] = 2.30; 95% confidence interval [CI] = 1.40, 3.78) and anxiety (aPR = 1.41; 95% CI = 1.05, 1.91). Prevalence of depression and anxiety were similar between HIV-infected and -uninfected participants. Estimates remained comparable when restricted to HIV-infected participants.
Mental disorders may be affected by food insecurity among new tuberculosis patients, regardless of HIV status.
In Spain, the epidemic curve caused by COVID-19 has reached its peak in the last days of March. The implementation of the blockade derived from the declaration of the state of alarm on 14th March has raised a discussion on how and when to deal with the unblocking. In this paper, we intend to add information that may help by using epidemic simulation techniques with stochastic individual contact models and several extensions.
We discuss a continuous-time Markov branching model in which each individual can trigger an alarm according to a Poisson process. The model is stopped when a given number of alarms is triggered or when there are no more individuals present. Our goal is to determine the distribution of the state of the population at this stopping time. In addition, the state distribution at any fixed time is also obtained. The model is then modified to take into account the possible influence of death cases. All distributions are derived using probability-generating functions, and the approach followed is based on the construction of families of martingales.
After the 2003 SARS epidemic, China started constructing a primary-level emergency response system and focused on strengthening and implementation of policies, resource allocation. After 17 years of restructuring, China's primary-level response capabilities towards public health emergencies have greatly improved. During the coronavirus disease 2019 epidemic, primary-level administrative and medical personnel, social organisations, volunteers, etc. have played a significant role in providing professional services utilising the primary-level emergency response system of 17 years. However, China's organisations did not learn their lesson from the SARS epidemic, and certain problems are exposed in the system. By analysing the experience and shortcomings of China's disease prevention and control system at the primary level, we can focus on the development of disease control systems for major epidemics in the future.
The coronavirus disease (COVID-19) pandemic is a disaster of unprecedented proportions with global repercussions. Psychological preparedness, the primed cognitive awareness and anticipation of dealing with emotional responses in an adverse situation, has assumed a compelling relevance during a health disaster of this magnitude.
An anonymized eSurvey was conducted in India to assess psychological preparedness toward the ongoing pandemic with a focus on knowledge, management of own and others’ emotional response, and anticipatory coping mechanisms among the survey population. An adapted version of the qualitative Psychological Preparedness for Natural Disaster Scale validated by the World Health Organization was widely circulated over the Internet and various social media platforms for assessment. Results are expressed as median ± standard deviation. Descriptive statistics were used and figures downloaded from surveymonkey.com.
Of the 1120 respondents (M:F 1.7:1, age 35 years ±14.1), most expressed a high level of perceived knowledge and confidence of managing COVID-19, such as awareness of the symptoms of the illness (95.1%), actions needed (94.4%), hospital to report to (88.9%), and emergency contact number (89.1%). A majority (95%) monitored regularly the news bulletins and scientific journals regarding COVID-19. However, nearly one-third (29.2%) could not assess their likelihood of developing COVID-19, and 17.5% were unaware of the difference between a mild and severe infection. Twenty-three percent (23.3%) were unfamiliar with the materials needed in an acute illness situation.
Psychological disaster preparedness is reasonable, although lacking in specific domains. Timely but focused interventions can be a cost-efficient administrative exercise, which federal agencies may prioritize working on.
The purpose of this research was to investigate coronavirus disease (COVID-19) susceptibility in districts of Bangladesh using multicriteria evaluation techniques.
Secondary data were collected from different government organizations, 120 primary surveys were conducted for calculating weights, and results were validated through 12 key people’s interviews. Pairwise comparison matrixes were calculated for 9 factors and subfactors. The analytic hierarchy process used for calculating the susceptibility index and map was prepared based on the results.
According to the results, multiple causal factors might be responsible for COVID-19 spreading in Bangladesh. Dhaka might be vulnerable to COVID-19 due to a higher population, population density, and international collaboration. According to the pairwise comparison matrix, the consistency ratio for subfactors and factors was in the permissible limit (ie, less than 0.10). The highest factor weight of 0.2907 was found for the factors type of port. The maximum value for the susceptibility index was 0.435219362 for Chittagong, and the minimum value was 0.076174 for Naogaon.
The findings of this research might help the communities and government agencies with effective decision-making.
The susceptible-infected-removed (SIR) model and its variants are widely used to predict the progress of coronavirus disease 2019 (COVID-19) worldwide, despite their rather simplistic nature. Nevertheless, robust estimation of the SIR model presents a significant challenge, particularly with limited and possibly noisy data in the initial phase of the pandemic.
The K-means algorithm is used to perform a cluster analysis of the top 10 countries with the highest number of COVID-19 cases, to observe if there are any significant differences among countries in terms of robustness.
As a result of model variation tests, the robustness of parameter estimates is found to be particularly problematic in developing countries. The incompatibility of parameter estimates with the observed characteristics of COVID-19 is another potential problem. Hence, a series of research questions are visited.
We propose a Single Parameter Estimation (SPE) approach to circumvent these potential problems if the basic SIR is the model of choice, and we check the robustness of this new approach by model variation and structured permutation tests. Dissemination of quality predictions is critical for policy- and decision-makers in shedding light on the next phases of the pandemic.
COVID-19 outbreak has surfaced as an imminent threat for the public health. Because India is a populous country, it is important for Indians to be aware of the basic modes of prevention that can diminish the spread of the coronavirus disease 2019 (COVID-19) infection.
The present questionnaire study was carried out among the undergraduate students to assess the awareness regarding the spread and control of COVID-19.
The questionnaire was circulated among the undergraduate students as a Google form.
The study included responses of 868 undergraduate students belonging to 2 university colleges. The majority of the participants were females (63%; n = 547) in the age range of 18-23 y. Approximately 98.3% (853) had awareness regarding COVID-19. Approximately 94.7% (822) were washing their hands after visiting public places, out of which only 90.6% (786) were aware of proper steps to be followed in hand washing. It was concluded that it is required to create awareness among 20.8% (181) of our study participants regarding the importance of hand washing to control COVID-19.
Awareness regarding COVID-19 among study participants was good. However, a small part of the study population is required to be educated on proper steps to be followed in hand washing.
The coronavirus disease 2019 (COVID 19) is a new viral zoonosis of global concern that could cause psychological sequelae. We examined the levels of psychological distress, anxiety, depression, and stress during the COVID-19 outbreak in a Mexican sample.
An online survey was applied that collected information on demographic and financial status data, physical status, contact history, knowledge, concerns, and precautionary measures concerning COVID-19. Impact of Event Scale-Revised and Depression, Anxiety, and Stress Scale were included.
A total of 50.3% of respondents rated psychological distress as moderate-severe; 15.7% reported moderate-severe depressive symptoms; 22.6% reported moderate-severe anxiety symptoms; and 19.8% reported moderate-severe stress levels. Female gender, older age, divorced status, lack of confidence related to security of the test, lower satisfaction of health information concerning COVID-19, history of direct or indirect contact with a COVID-19 confirmed case, live with just 1 other person, and spent >9 h/d at home were associated with greater psychological distress and/or higher levels of stress, anxiety, and depression. By contrast, precautionary measures, such as hand hygiene and wearing masks, were associated with lower levels of psychological distress, depression, anxiety, and stress.
COVID-19 outbreak results in considerable psychological effects among the Mexican sample.
The aim of this study was to estimate the basic reproduction number (R0) of COVID-19 in the early stage of the epidemic and predict the expected number of new cases in Shahroud in Northeastern Iran. The R0 of COVID-19 was estimated using the serial interval distribution and the number of incidence cases. The 30-day probable incidence and cumulative incidence were predicted using the assumption that daily incidence follows a Poisson distribution determined by daily infectiousness. Data analysis was done using ‘earlyR’ and ‘projections’ packages in R software. The maximum-likelihood value of R0 was 2.7 (95% confidence interval (CI): 2.1−3.4) for the COVID-19 epidemic in the early 14 days and decreased to 1.13 (95% CI 1.03–1.25) by the end of day 42. The expected average number of new cases in Shahroud was 9.0 ± 3.8 cases/day, which means an estimated total of 271 (95% CI: 178–383) new cases for the period between 02 April to 03 May 2020. By day 67 (27 April), the effective reproduction number (Rt), which had a descending trend and was around 1, reduced to 0.70. Based on the Rt for the last 21 days (days 46–67 of the epidemic), the prediction for 27 April to 26 May is a mean daily cases of 2.9 ± 2.0 with 87 (48–136) new cases. In order to maintain R below 1, we strongly recommend enforcing and continuing the current preventive measures, restricting travel and providing screening tests for a larger proportion of the population.
In times of crisis, people have historically had to band together to overcome. What happens when they cannot? This article examines the reality of people forced to isolate from one another during one of the most turbulent events of their lives: the COVID-19 pandemic. Connecting the dots of topics including fear, social stigmas, global public response and previous disease outbreaks, this article discusses the negative mental health effects that individuals and communities will likely suffer as the result of social distancing, isolation and physical infection.
There have been multiple inconsistencies in the manner the COVID-19 pandemic has been investigated and managed by countries. Population-based management (PBM) has been inconsistent, yet serves as a necessary first step in managing public health crises. Unfortunately, these have dominated the landscape within the United States and continue as of this writing. Political and economic influences have greatly influenced major public health management and control decisions. Responsibility for global public health crises and modeling for management are the responsibility of the World Health Organization (WHO) and the International Health Regulations Treaty (IHR). This review calls upon both to reassess their roles and responsibilities that must be markedly improved and better replicated world-wide in order to optimize the global public health protections and its PBM.
“Ask a big enough question, and you need more than one discipline to answer it.”
Liz Lerman, MacArthur “Genius” Fellow, Choreographer, Modern Dance legend, and 2011 Artist-in Residence, Harvard Music Department
Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is a form of an infectious respiratory disease, discovered in November 2012 in Saudi Arabia. According to the World Health Organization (WHO; Geneva, Switzerland) reports, a total of 2,519 laboratory-confirmed cases and 866 MERS-CoV-related deaths were recorded as of March 5, 2016.1 The majority of reported cases originated from Saudi Arabia (2,121 cases). Also, MERS-CoV is believed to be of zoonotic origin and has been linked to camels in the Arabian area.1,2 In this report, the authors discuss the lessons learned from the MERS-CoV outbreak at King Abdul-Aziz Medical City-Riyadh (KAMC-R) from August through September 2015 from the Emergency Medical Services (EMS) perspective. The discussion includes the changes in policies and paramedic’s practice, the training and education in infection control procedures, and the process of transportation of these cases. The authors hope to share their experience in this unique situation and highlight the preparedness and response efforts that took place by the division of EMS during the outbreak.
In December, 2019, an infectious outbreak of unknown cause occurred in Wuhan, which attracted intense attention. Shortly after the virus was identified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the epidemic of coronavirus disease 2019 (COVID-19) broke out, and an information storm occurred. At that time, 2 important aspects, that is, the stages of spread and the components of the epidemic, were unclear. Answers to the questions (1) what are the sources, (2) how do infections occur, and (3) who will be affected should be clarified as the outbreak continues to evolve. Furthermore, components of the epidemic and the stages of spread should be explored and discussed. Based on information of SARS, Middle East respiratory syndrome (MERS), and COVID-19, the components of the epidemic (the sources, the routes of infection, and the susceptible population) will be discussed, as well as the role of natural and social factors involved. Epidemiologic characteristics of patients will be traced based on current information.
As colleges and universities respond to the COVID-19 outbreak, many in the media call it unprecedented. This is not the first time that institutions of higher education have had to respond to an epidemic, however. A historical review of college and university reactions to illnesses such as yellow fever and the 1918 influenza pandemic provides prior examples of institutional responses to epidemic diseases.
The objective of this paper is to prepare the government and citizens of India to take or implement the control measures proactively to reduce the impact of coronavirus disease 2019 (COVID-19).
In this work, the COVID-19 outbreak in India has been predicted based on the pattern of China using a machine learning approach. The model is built to predict the number of confirmed cases, recovered cases, and death cases based on the data available between January 22, 2020, and April 3, 2020. The time series forecasting method is used for prediction models.
The COVID-19 effects are predicted to be at peak between the third and fourth weeks of April 2020 in India. This outbreak is predicted to be controlled around the end of May 2020. The total number of predicted confirmed cases of COVID-19 might reach around 68 978, and the number of deaths due to COVID-19 are predicted to be 1557 around April 25, 2020, in India. If this outbreak is not controlled by the end of May 2020, then India will face a severe shortage of hospitals, and it will make this outbreak even worse.
The COVID-19 pandemic may be controlled if the Government of India takes proactive steps to aggressively implement a lockdown in the country and extend it further. This presented epidemiological model is an effort to predict the future forecast of COVID-19 spread, based on the present scenario, so that the government can frame policy decisions, and necessary actions can be initiated.
Chapter 7 highlights the centrality of the history of Mediterranean plague and quarantine to the birth of the public health movement in Britain. Even though bubonic plague is often considered to be a premodern problem, its diffuse and dramatic reputation thoroughly shaped conceptions of other nineteenth-century killer epidemics –– cholera in particular. The chapter reconsiders the much-discussed “contagion debate” within this wider, transnational genealogy of public health. The fight between those who believed epidemic disease was communicated by contact and proximity (“contagionists”) and those who believed that epidemics spread because of atmospheric factors, such as temperature, winds, marsh exhalations, or other putrefying matter (“anticontagionists” or “miasmatists”) has achieved a tired reputation in recent historiography, which casts it as professional posturing in the midst of broad agreement. While this may be true when it comes to cholera, by focusing on quarantine and plague, the broader significance of these medical arguments is more readily apparent. In part thanks to quarantine, public health reformers tended to present problems in explicitly national terms or within dichotomies of national versus foreign. Because of the way they undergirded this national framing, plague and quarantine are an influential part of the genesis of what has been called the “Condition of England Question.”