Epidemic Outbreaks Research Articles

Dynamic Systems with Fractional Derivatives Applied to Interagent Populations Problems

The present work has as main objective to investigate the use of fractional calculus in the modeling of epidemic outbreaks of interacting populations. In particular, we propose a generalization of the SIR model with fractional derivatives to describe the dynamics of the COVID-19 epidemic outbreak in two cities with interacting populations. In special, we consider the dynamics of COVID-19 in the municipalities of Pelotas and Rio Grande, which are neighboring cities and are relatively geographically isolated from the rest of the state of Rio Grande do Sul.

Foresighted medical resources allocation during an epidemic outbreak

While some reports show that the existing real-life medical resources allocations during epidemic outbreaks are myopic, some experts claim that medical resources allocations based on foresighted future allocations might enable a better balance of supply and demand. To investigate this claim, we develop a foresighted medical resources allocation model to help governments manage large-scale epidemic outbreaks. We formulate a demand forecasting model with a general demand forecasting function based on the last-period demands, extra demand caused by the last-period unfulfilled demand, and uncertain demand. In the foresighted allocation model, the government decides the current-period allocation based on the foresighted demand, which considers the last-period area demand and uncertain demand from the current period to the end of a planning horizon, using a stochastic dynamic program. We find that the optimal allocation is a function of the allocation capacity in each period. The optimal foresighted allocation is always higher than the optimal static (one-period) allocation and decreases with allocation capacity. When the allocation capacity is sufficiently large, the foresighted demand is close to the static demand. Besides, if the cost of oversupply is close to zero, the optimal allocations for both the foresighted allocation and one-period models are the allocation capacity. Our results provide useful managerial implications for a government contemplating medical resources allocation in response to an epidemic outbreak.

Suggestions of management on emergency responses to epidemic outbreaks in outpatient or emergency departments of tertiary teaching hospitals

Image, graphical abstract.

An interrupted time series analysis of hospital admissions due to alcohol intoxication during the COVID‐19 pandemic in Tehran, Iran

The COVID-19 outbreak affected mental health globally. One of the major concerns following the COVID-19 pandemic was increased incidence of risky behaviors including alcohol consumption. This study evaluates the trend of alcohol poisoning in Loghman-Hakim Hospital (LHH), the main referral center of poisoning in Tehran, during the 2-year period from 1 year prior to 1 year after the onset (February 23rd, 2020) of the COVID-19 epidemic in Iran. All patients admitted with alcohol intoxication from February 23rd, 2019 to February 22nd, 2021 were evaluated and patient data extracted from LHH electronic hospital records. Alcohols were categorized as toxic (methyl alcohol) and non-toxic (ethyl alcohol). Of 2483 patients admitted, 796/14,493 (5.49%) and 1687/13,883 (12.15%) had been hospitalized before and after the onset of the COVID-19 epidemic in Iran, respectively. In total, 140 patients did not survive, of whom 131 (93.6%) were confirmed to have methanol intoxication. Mortality was significantly higher during the outbreak (127 vs 13; P < 0.001; OR: 4.90; CI 95%: 2.75 to 8.73). Among the patients, 503 were younger than age 20. Trend of alcohol intoxication showed increases in children (57 vs 17) and adolescents (246 vs 183) when compared before and after the COVID-19 epidemic outbreak. A total of 955 patients were diagnosed with methanol toxicity which occurred more frequently during the COVID-19 era (877 vs 78; P < 0.001; OR: 10.00; CI 95%: 7.75 to12.82). Interrupted time series analysis (April 2016–February 2021) showed that in the first month of the COVID-19 epidemic (March 2020), there was a significant increase in the alcohol intoxication rate by 13.76% (P < 0.02, CI = [2.42–24.91]). The trend of alcohol intoxication as well as resulting mortality increased in all age groups during the COVID-19 epidemic in Iran, indicating urgent need for the prevention of high-risk alcohol use as well as improved treatment.

Study on the optimization of urban emergency supplies distribution paths for epidemic outbreaks

The humanitarian logistics vehicle routing problem (VRP) of urban emergency supplies in response to epidemic outbreaks is studied in this work to address the adverse effects of epidemic outbreaks on human society. Under the premise that the greater the urgency of the demand for various emergency supplies by the hospitals in the city’s jurisdiction, the higher the priority of distribution, the fitness function of the highest vehicle utilization rate and the lowest transportation cost is established. The aim of the research is to construct a multi-objective mathematical model of emergency supplies distribution for large cities in case of an epidemic outbreak. Based on the multi-verse optimization algorithm and the differential evolution algorithm, a hybrid multi-verse optimizer algorithm based on differential evolution (DE-IMOV) is proposed. A simulation experiment is then carried out to verify the model. The calculation results show that the developed algorithm can effectively reduce the distribution cost and improve the full load rate of vehicles on the basis of considering the urgency of emergency supplies demand, which provides a solution for the optimization of heterogeneous vehicle distribution paths for urban emergency supplies in response to epidemic outbreaks.

Intervention strategies for epidemic spreading on bipartite metapopulation networks.

Intervention strategies are of great significance for controlling large-scale outbreaks of epidemics. Since the spread of epidemic depends largely on the movement of individuals and the heterogeneity of the network structure, understanding potential factors that affect the epidemic is fundamental for the design of reasonable intervention strategies to suppress the epidemic. So far, most of previous studies mainly consider intervention strategies on the network composed of a single type of locations, while ignoring the movement behavior of individuals to and from locations that are composed of different types, i.e., residences and public places, which often presents heterogeneous structure. In addition, the transmission rate in public places with different population flows is heterogeneous. Inspired by the above observation, we build a bipartite metapopulation network model and propose intervention strategies based on the importance of public places. With the Markovian Chain approach, we derive the epidemic threshold under intervention strategies. Experimental results show that, compared with the uniform intervention to residences or public places, nonuniform intervention to public places is more effective for suppressing the epidemic with an increased epidemic threshold. Specifically, interventions to public places with large degree can further suppress the epidemic. Our study opens a new path for understanding the spatial epidemic spread and provides guidance for the design of intervention strategies for epidemics in the future.

How many masks do you buy? A simple dilemma task to differentiate between individual and social rationality.

Prior measures on rationality overlook the individual differences in the weight people place on social rationality versus individual rationality. The current research develops and validates an individual-collective dilemma task (ICDT) to distinguish different rationality types. It was translated from a reality that, at the beginning of the ongoing COVID-19 outbreak, a global shortage of face masks occurred because of the jumping demand for masks as a precautionary measure. The ICDT asked participants to decide how many masks to buy in front of a shortfall of masks, which facilitated coping with a hypothetical epidemic outbreak. Based on the number of masks they selected, three rationality groups emerged. Individual rationalists preferred self-interest goals to goals of social interests; social rationalists prioritized social-interest goals; balancers assigned equal weight to both goals. The ICDT showed sound test–retest reliability and criterion-related, discriminant, and convergent validity. The present research contributes to the literature on rationality assessment and offers policy-makers a valid and reliable tool to understand the distribution of rationalists among the public.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12144-022-03338-x.

Impact of yoga on the level of distress of parents having children with intellectual disability undergoing home based education during COVID-19 crises

COVID-19 is now spreading across the country as well as worldwide, and poses some public those have more responsibilities of his/ her life and they are may be suffering more mental health problems which require close attention. This study aims to assess the effect of yoga exercise on distress level of parents having children with Intellectual disability and doing or not doing yoga exercise during implementing Home Based Education. Researchers compiled a 35-item scale for five dimensions. 40 parents having children with Intellectual disability were selected for this study and survey was conducted through an online questionnaire. ANOVA test was applied to identify the impact of yoga on distress. Distress was significant based on doing yoga exerce and perceived impacts of the epidemic outbreak.

Epidemic model with strain-dependent transmission rate

Persistent epidemic can lead to the emergence of new virus strains due to virus mutations. This work is devoted to the SIR model with strain-dependence of infected individuals due to virus mutations and a continuous strain variable. Characterization of epidemic progression is obtained for a strain-dependent infectivity function in numerical simulations and with some analytical estimates. Taking into account limited infection-induced immunity, transition from recovered to susceptible compartment is considered. Different scenarios of epidemic progression are identified. In the case of a monotonically growing transmission rate as a function of strain, only one epidemic outbreak is observed. However, if the transmission rate is a non-monotonic function, then multiple outbreaks can occur.

Long-term spatial and population-structured planning of non-pharmaceutical interventions to epidemic outbreaks

In this paper, we consider the problem of planning non-pharmaceutical interventions to control the spread of infectious diseases. We propose a new model derived from classical compartmental models; however, we model spatial and population-structure heterogeneity of population mixing. The resulting model is a large-scale non-linear and non-convex optimisation problem. In order to solve it, we apply a special variant of covariance matrix adaptation evolution strategy. We show that results obtained for three different objectives are better than natural heuristics and, moreover, that the introduction of an individual’s mobility to the model is significant for the quality of the decisions. We apply our approach to a six-compartmental model with detailed Poland and COVID-19 disease data. The obtained results are non-trivialand sometimes unexpected; therefore, we believe that our model could be applied to support policy-makers in fighting diseases at the long-term decision-making level.

Epidemic risk assessment from geographic population density

The geographic distribution of the population on a region is a significant ingredient in shaping the spatial and temporal evolution of an epidemic outbreak. Heterogeneity in the population density directly impacts the local relative risk: the chances that a specific area is reached by the contagion depend on its local density and connectedness to the rest of the region. We consider an SIR epidemic spreading in an urban territory subdivided into tiles (i.e., census blocks) of given population and demographic profile. We use the relative attack rate and the first infection time of a tile to quantify local severity and timing: how much and how fast the outbreak will impact any given area. Assuming that the contact rate of any two individuals depends on their household distance, we identify a suitably defined geographical centrality that measures the average connectedness of an area as an efficient indicator for local riskiness. We simulate the epidemic under different assumptions regarding the socio-demographic factors that influence interaction patterns, providing empirical evidence of the effectiveness and soundness of the proposed centrality measure.

The Media Briefings of the MOHAP and its Communication Content to Raise Awareness of the Coronavirus

At the outbreak of disease and epidemics, governments and health-care organizations prepare emergency crisis management plans, developed based on information and guidance from the World Health Organization. These include preparedness, response and recovery strategies, media and public communication are an important component of their implementation. The concept of crisis communication implies that honest, candid, quick, accurate and full response to the crisis is one of the basic requirements of crisis management. The study aims to reveal the type of communication content presented by the media briefings of the MOHAP awareness of the Coronavirus and to curb its spread. The study adopted a curriculum for content analysis(quantitative and qualitative)of the periodic briefings organized by the Ministry over four months (March, April, May, June), starting from the first briefing held on Saturday, March 7, 2020 ended with the last regular briefing on Tuesday, June 27, 2020. The study found several results, the most important of which were: The MOHAP has been quick, organized and effective in its communication response; Periodic briefings were organized on the latest developments and figures regarding Coronavirus cases in the State, government actions and initiatives announced to curb the spread of COVID19. The study recommends that further studies be undertaken to implement the CERC Crisis and Risk Management Communications Model, in order to reveal the effectiveness of crisis communication applied by the bodies and organizations in the Arab region to manage crises and risks, both in communications and in the media

Shigellosis in Poland in 2018-2019.

The purpose of this study is evaluation of the epidemiological situation of shigellosis in Poland in years 2018-2019, comparing to previous years. The evaluation of epidemiological situation of shigellosis in Poland was based on analysis of data from epidemiological surveillance collected in questionnaires and sent to the Department of Epidemiology of Infectious Diseases and Surveillance NIPH NIH-NRI by sanitary stations. Once a case was reported on a ZLK-1 and/or ZLB-1 form to the appropriate State Sanitary Inspector, an epidemiological investigation was conducted by collecting additional information from medical records and from the patient interview. Data from the "Infectious Diseases and Poisonings" bulletins years 2012-2019 was also used as well as data from shigellosis outbreaks acquired through ROE system - registry created for outbreak data gathering. In Poland in years 2018-2019 a total of 321 cases of shigellosis was reported. Incidence per 100,000 was 0.74 in 2018, which was considerably more than in previous years, and in 2019 was 0.10 - similarly as two years before. The most cases belonged to the 25-34 and 35-44 age groups, and the least cases to the 0-4 age group. Majority of them were women. Percentage of hospitalizations was 27% in described period. Regarding to previous years, in 2018 decrease of hospitalizations was noted. Imported cases were 54.8% of all with predominance of imports from Albania. Twenty eight percent of infections were caused by S. sonnei species. In discussed years 10 outbreaks were registered with 246 people ill, including two large outbreaks (77 and 150 cases). August was the month with the highest number of cases (217 cases, 67.6% of all), 62.2% of them were imported. Increase in number of cases and incidence of shigellosis in Poland in 2018 was caused by occurrence of two large epidemic outbreaks. Because of high percentage of imported cases, traveling to shigellosis endemic countries is currently the main risk factor for contracting the disease. This indicates the need for education for those who travel to endemic areas.

Study on Invulnerability of Material Emergency Transportation System Based on Three-Layer Interdependent Network

The material transportation capacity under emergency conditions is an important guarantee for the country to deal with war, epidemic outbreak, and other crisis situations. Under emergency conditions, some nodes of the material transportation system may fail to work normally, which may lead to the collapse of the whole system. Based on analyzing the characteristics of the material emergency transportation system, this article builds a three-layer interdependent network model and uses the improved M-L model to describe the failure propagation mechanism of node damage in the three-layer network. Then, the network model is attacked randomly, and the relationship between invulnerability of the three-layer network and the three main indexes of network flow, average degree, and probability of interdependence are studied. Afterwards, the propagation of cascading failure among three subnetworks in the interdependent network is analyzed and compared. This article provides a theoretical basis for building an efficient and robust material emergency transportation system.

An equitable and accessible vaccine supply chain network in the epidemic outbreak of COVID-19 under uncertainty.

Vaccination is one of the most efficient ways to restrict and control the spread of epidemic outbreaks such as COVID-19. Due to the limited COVID-19 vaccine supply, an equitable and accessible plan should be prepared to cope with. This research focuses on designing a vaccine supply chain while aiming to achieve an equitable and accessible network. We present a novel mathematical formulation that helps to optimize vaccine distribution to inoculate people with various priority levels to achieve an equitable plan. The transshipment strategy is also incorporated into the model to enhance the accessibility of COVID-19 vaccine types between health facilities. The nature of COVID-19 is dynamic over time due to mutations, and the protection level of each vaccine type against this disease is not exact. Besides, complete information about the demand for different vaccine types is not available. Hence, we use Multi-Stage Stochastic Programming as a reliable strategy that is organized to manage stochastic data in a dynamic environment for the first time in the vaccine supply chain network. The scenarios in this approach are generated using a Monte Carlo simulation method, and then a forward scenario reduction technique is conducted to construct a suitable scenario tree. The practicality and capability of the model are shown in a real-life case of Iran. The results show that the performance of the Multi-Stage Stochastic Programming is significantly improved compared with the two-stage stochastic programming regarding the total cost of the vaccine supply chain and the number of the shortage units.

A systematic review and meta-analysis on prevalence of and risk factors associated with depression, anxiety and insomnia in infectious diseases, including COVID-19: a call to action.

Infectious disease epidemics have become more frequent and more complex during the 21st century, posing a health threat to the general public and leading to psychological symptoms. The current study was designed to investigate the prevalence of and risk factors associated with depression, anxiety and insomnia symptoms during epidemic outbreaks, including COVID-19. We systematically searched the PubMed, Embase, Web of Science, OVID, Medline, Cochrane databases, bioRxiv and medRxiv to identify studies that reported the prevalence of depression, anxiety or insomnia during infectious disease epidemics, up to August 14th, 2020. Prevalence of mental symptoms among different populations including the general public, health workers, university students, older adults, infected patients, survivors of infection, and pregnant women across all types of epidemics was pooled. In addition, prevalence of mental symptoms during COVID-19 was estimated by time using meta-regression analysis. A total of 17,506 papers were initially retrieved, and a final of 283 studies met the inclusion criteria, representing a total of 948,882 individuals. The pooled prevalence of depression ranged from 23.1%, 95% confidential intervals (95% CI: [13.9–32.2]) in survivors to 43.3% (95% CI: [27.1–59.6]) in university students, the pooled prevalence of anxiety ranged from 25.0% (95% CI: [12.0–38.0]) in older adults to 43.3% (95% CI: [23.3–63.3]) in pregnant women, and insomnia symptoms ranged from 29.7% (95% CI: [24.4–34.9]) in the general public to 58.4% (95% CI: [28.1–88.6]) in university students. Prevalence of moderate-to-severe mental symptoms was lower but had substantial variation across different populations. The prevalence of mental problems increased over time during the COVID-19 pandemic among the general public, health workers and university students, and decreased among infected patients. Factors associated with increased prevalence for all three mental health symptoms included female sex, and having physical disorders, psychiatric disorders, COVID infection, colleagues or family members infected, experience of frontline work, close contact with infected patients, high exposure risk, quarantine experience and high concern about epidemics. Frequent exercise and good social support were associated with lower risk for these three mental symptoms. In conclusion, mental symptoms are common during epidemics with substantial variation across populations. The population-specific psychological crisis management are needed to decrease the burden of psychological problem and improve the mental wellbeing during epidemic.

First Description of Serological Evidence for SARS-CoV-2 in Lactating Cows.

Simple SummarySevere Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is the agent of the disease that has caused a global pandemic, known as coronavirus disease 2019 (COVID-19). Coronaviruses (CoVs) may emerge from wildlife hosts and infect humans and animals. Up to now, natural infection with SARS-CoV-2 has been reported in several animals, but it has not been found in farm animals, such as buffaloes, goats, sheep, horses, rabbits, hens, pigs, or cows, despite contact with their SARS-CoV-2-positive human breeders. Furthermore, a low susceptibility to SARS-CoV-2 has been detected in experimentally infected cattle with SARS-CoV-2. The unknown zoonotic potential of this virus is a cause of concern for pet owners and farmers. The limited data on cattle suggest that cattle show low susceptibility to SARS-CoV-2 and probably do not function as reservoirs. However, in areas with large cattle populations and a high prevalence of SARS-CoV-2 infection in humans, close contact between livestock and farmworkers may cause reverse zoonotic infections in cattle, as has already been described for highly sensitive animal species, such as minks, cats, and dogs. Thus, studying the zoonotic characteristics of SARS-CoV-2 could help in the development of a strategy for virus detection and the control of viral dissemination.Following the COVID-19 epidemic outbreak in Ariano Irpino, Campania region (Italy), we tested lactating cows for the presence of SARS-CoV-2 on a cattle farm at which, prior to the investigation, 13 of the 20 farmworkers showed COVID-19-like symptoms, and one of them died. Twenty-four lactating cows were sampled to detect SARS-CoV-2. All nasal and rectal swabs and milk samples were negative for SARS-CoV-2 RNA. Of the 24 collected serum samples, 11 showed antibodies against SARS-CoV-2 nucleocapsid protein, 14 showed antibodies against SARS-CoV-2 spike protein, and 13 developed neutralising antibodies for SARS-COV-2; all samples were negative for Bovine Coronavirus (BCoV), another betacoronavirus. To our knowledge, this is the first report of natural serological evidence of SARS-CoV-2 infection in lactating cows. We hypothesise that this may be a case of reverse zoonosis. However, the role of cattle in SARS-CoV-2 infection and transmission seems to be negligible.

The Never-Ending Poxes of Syphilis, AIDS, and Measles

In this article, I address some infectious diseases that never really “ended,” even though their morbidity, their social impact, and their public visibility have faded away: AIDS, syphilis, and measles. I will use data from different projects I have conducted on each of those epidemics: HIV/AIDS at the doctoral training level in the 1990s, with a geographical focus on Brazil and the United States; syphilis in the context of a 2010 project on the social history of health in Lisbon in the late 19th and early 20th centuries; and measles as part of my current project on labor migration in the 19th century, with a focus on epidemic outbreaks in migrant ships from Madeira to Hawaii.

Functional data analysis: An application to COVID‐19 data in the United States in 2020

BackgroundIn this paper, we conduct an analysis of the COVID‐19 data in the United States in 2020 via functional data analysis methods. Through this research, we investigate the effectiveness of the practice of public health measures, and assess the correlation between infections and deaths caused by the COVID‐19. Additionally, we look into the relationship between COVID‐19 spread and geographical locations, and propose a forecasting method to predict the total number of confirmed cases nationwide.MethodsThe functional data analysis methods include functional principal analysis methods, functional canonical correlation analysis methods, an expectation‐maximization (EM) based clustering algorithm and a functional time series model used for forecasting.ResultsIt is evident that the practice of public health measures helps to reduce the growth rate of the epidemic outbreak over the nation. We have observed a high canonical correlation between confirmed and death cases. States that are geographically close to the hot spots are likely to be clustered together, and population density appears to be a critical factor affecting the cluster structure. The proposed functional time series model gives more reliable and accurate predictions of the total number of confirmed cases than standard time series methods.ConclusionsThe results obtained by applying the functional data analysis methods provide new insights into the COVID‐19 data in the United States. With our results and recommendations, the health professionals can make better decisions to reduce the spread of the epidemic, and mitigate its negative effects to the national public health.

Historical Epidemiology and the Single Pathogen Model of Epidemic Disease

Pre-existing medical conditions and co-infections are common to all human populations, although the natures of the pre-existing conditions and the types of co-infections vary. For these reasons, among others, the arrival of a highly infectious pathogenic agent may differentially affect the disease burden in different sub-populations, as a function of varying combinations of endemic disease, chronic disease, genetic or epigenetic vulnerabilities, compromised immunological status, and socially determined risk exposure. The disease burden may also vary considerably by age cohort and socio-economic status. The social consequences of infection, the medical sequelae, and the paths of recovery from infection may also vary, according to these variables. As a result, different sub-populations may have different experiences of a disease process, including when and how an epidemic comes to an end. This essay suggests that historians' engagement with path-breaking biological and medical anthropological research will allow new approaches to understanding disease processes that will enrich the study of epidemics and their endings. It is organized in three sections. The first discusses some general approaches to and assumptions about epidemics that are used by many historians. The second introduces new perspectives on the study of historical epidemics opened up by the medical anthropological concept of syndemic disease interactions and by an engagement with biological scientific perspectives. It briefly discusses two rural epidemics-one of anemia caused by the syndemic interaction of hookworm and malaria co-infections in early 20th-century British Malaya, and the other of rebound infections caused by the loss of acquired immunity to falciparum malaria in mid-20th-century Liberia-that illustrate, respectively, the utility of the concept of syndemic disease interactions and the centrality of immunological status in understanding epidemic outbreaks in these discrete populations. The third section addresses some of the research considerations involved in moving beyond the single pathogen model of epidemic disease.