The discrete-time Kermack-McKendrick model: A versatile and computationally attractive framework for modeling epidemics.

The COVID-19 pandemic has led to numerous mathematical models for the spread of infection, the majority of which are large compartmental models that implicitly constrain the generation-time distribution. On the other hand, the continuous-time Kermack-McKendrick epidemic model of 1927 (KM27) allows an arbitrary generation-time distribution, but it suffers from the drawback that its numerical implementation is rather cumbersome. Here, we introduce a discrete-time version of KM27 that is as general and flexible, and yet is very easy to implement computationally. Thus, it promises to become a very powerful tool for exploring control scenarios for specific infectious diseases such as COVID-19. To demonstrate this potential, we investigate numerically how the incidence-peak size depends on model ingredients. We find that, with the same reproduction number and the same initial growth rate, compartmental models systematically predict lower peak sizes than models in which the latent and the infectious period have fixed duration.

An SEIR Model with Time-Varying Coefficients for Analyzing the SARS-CoV-2 Epidemic.

In this study, we propose a time-dependent susceptible-exposed-infected-recovered (SEIR) model for the analysis of the SARS-CoV-2 epidemic outbreak in three different countries, the United States, Italy, and Iceland using public data inherent the numbers of the epidemic wave. Since several types and grades of actions were adopted by the governments, including travel restrictions, social distancing, or limitation of movement, we want to investigate how these measures can affect the epidemic curve of the infectious population. The parameters of interest for the SEIR model were estimated employing a composite likelihood approach. Moreover, standard errors have been corrected for temporal dependence. The adoption of restrictive measures results in flatten epidemic curves, and the future evolution indicated a decrease in the number of cases.

A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media.

Coronaviruses are a well-established and deadly group of viruses that cause illness in both humans and animals. The novel type of this virus group, named COVID-19, was firstly reported in December 2019, and, with the passage of time, coronavirus has spread to almost all parts of the world. Coronavirus has been the cause of millions of deaths around the world. Furthermore, many countries are struggling with COVID-19 and have experimented with various kinds of vaccines to eliminate the deadly virus and its variants. This survey deals with COVID-19 data analysis and its impact on human social life. Data analysis and information related to coronavirus can greatly help scientists and governments in controlling the spread and symptoms of the deadly coronavirus. In this survey, we cover many areas of discussion related to COVID-19 data analysis, such as how artificial intelligence, along with machine learning, deep learning, and IoT, have worked together to fight against COVID-19. We also discuss artificial intelligence and IoT techniques used to forecast, detect, and diagnose patients of the novel coronavirus. Moreover, this survey also describes how fake news, doctored results, and conspiracy theories were spread over social media sites, such as Twitter, by applying various social network analysis and sentimental analysis techniques. A comprehensive comparative analysis of existing techniques has also been conducted. In the end, the Discussion section presents different data analysis techniques, provides future directions for research, and suggests general guidelines for handling coronavirus, as well as changing work and life conditions.

An empirical study on consumer automobile purchase intentions influenced by the COVID-19 outbreak.

The sudden onset of the coronavirus disease 2019 (COVID-19) may influence individuals' automobile purchase decisions, thus bringing great uncertainty to the automobile industry. To this end, the current study investigates individuals' behaviors regarding the purchase of automobiles, both before and after the outbreak of COVID-19. An ICLV (integrated choice and latent variable) model that integrates the socio-demographics, epidemic-related variables and psychological latent variables is applied. A survey of 960 respondents was conducted in China during the epidemic. The results suggest that there was an increase in the demand for automobiles after the COVID-19 outbreak. Firstly, demand was especially high in the groups of females, citizens, high-income earners, and people who own a driving license or who live in high epidemic risk areas. Secondly, although the severity of the epidemic for residences has a positive effect on automobile demand, travelers' perceived vulnerability is the key factor motivating purchases. Thirdly, the epidemic's negative income effects reduced the purchase propensity. Several dynamic policies are proposed to automobile consumption of the special time of the COVID-19 pandemic.

Spatiotemporal behaviors of the ridership of a public transportation system during an epidemic outbreak: case of MERS in Seoul.

During May and June 2015, an outbreak of the Middle East respiratory syndrome (MERS) occurred in Korea, which raised the fear of contagion throughout society and suppressed the use of public transportation systems. Exploring daily ridership data of the Seoul bus transportation system, along with the number of infected patients and search volume in web portals, we observe that ridership decreased abruptly while attention was heavily focused online. Then this temporal reduction recovered exponentially with a characteristic time of 3 weeks when newly confirmed cases began to decrease. We also find with the data of ranked keywords of web portals that areas with severely reduced ridership tended to cluster and spatiotemporal variations of such clusters were highly associated with general hospitals where MERS patients were treated. On the other hand, the spatial reduction in ridership relaxed algebraically with the distance from a general hospital while the outbreak was severe. We further probe the influence of the epidemic outbreak in the framework of linear response theory, which relates the responses to the epidemic outbreak ("perturbation") with correlations in the absence of the perturbation. Indeed, the spatial correlation function of the ridership changes is observed to follow a power law, sharing the same exponent as the spatial relaxation of the response function. This new theoretical approach offers a useful tool for understanding responses of public transportation system to epidemic or accidental disasters.

[The epidemic process of whooping cough at contemporary stage in the Republic of Kyrgyzstan].

The causes of epidemic outbreaks of pertussis infection in the Kyrgyz Republic were studied in order to optimize immunological surveillance of this infection. The object of the study was the epidemic process of whooping cough, and the subject of the study was the incidence of pertussis infection in 2009-2018 and official data on the outbreak of pertussis in 2018. To diagnose pertussis the bacteriological method was applied. The bacteriological inoculation of the samples was carried out in the laboratory of the Republican Clinical Infectious Diseases Hospital. The smear from posterior pharyngeal wall was collected from 2153 patients. The level of pertussis antibodies was determined by enzyme-linked immunosorbent assay (ELISA) using the RIDASCREEN Pertussis IgG test system (R-Biopharm, Germany) in various series. The study data testifies that despite the vaccine prevention and high inoculation coverage, the epidemic increases occurred in incidence of whooping cough in 2015 and 2018 with an intensive rate of 4.7 and 9.6 per hundred thousand of population, respectively. The evaluation of vaccination status of patients demonstrated that out of them 80.7% were non-immunized; the percentage of vaccinated patients made up to 13.1%. The analysis of the age structure testifies that the main group of the diseased consisted of children under one year of life (63.1%), the second group consisted of children aged 1-4 years (33.1%). The severe forms of infection were observed among children under one year of age (95.8%). According to the territorial distribution, the largest percentage of cases fall on Bishkek - 70% (426 cases) and Chuyskaya Oblast - 22.4% (137 cases). The sero-epidemiological study revealed high proportion of seronegative individuals in all studied groups, and the highest percentage was observed in the group of children 5-9 years old and adolescents of 15-19 years old - 62.8% and 62%, respectively.

Optimal deployment of resources for maximizing impact in spreading processes.

The effective use of limited resources for controlling spreading processes on networks is of prime significance in diverse contexts, ranging from the identification of "influential spreaders" for maximizing information dissemination and targeted interventions in regulatory networks, to the development of mitigation policies for infectious diseases and financial contagion in economic systems. Solutions for these optimization tasks that are based purely on topological arguments are not fully satisfactory; in realistic settings, the problem is often characterized by heterogeneous interactions and requires interventions in a dynamic fashion over a finite time window via a restricted set of controllable nodes. The optimal distribution of available resources hence results from an interplay between network topology and spreading dynamics. We show how these problems can be addressed as particular instances of a universal analytical framework based on a scalable dynamic message-passing approach and demonstrate the efficacy of the method on a variety of real-world examples.

Quantifying the effects of quarantine using an IBM SEIR model on scalefree networks.

The COVID-19 pandemic led several countries to resort to social distancing, the only known way to slow down the spread of the virus and keep the health system under control. Here we use an individual based model (IBM) to study how the duration, start date and intensity of quarantine affect the height and position of the peak of the infection curve. We show that stochastic effects, inherent to the model dynamics, lead to variable outcomes for the same set of parameters, making it crucial to compute the probability of each result. To simplify the analysis we divide the outcomes in only two categories, that we call best and worst scenarios. Although long and intense quarantine is the best way to end the epidemic, it is very hard to implement in practice. Here we show that relatively short and intense quarantine periods can also be very effective in flattening the infection curve and even killing the virus, but the likelihood of such outcomes are low. Long quarantines of relatively low intensity, on the other hand, can delay the infection peak and reduce its size considerably with more than 50% probability, being a more effective policy than complete lockdown for short periods.

Implementation and evaluation of an algorithm for the management of scabies outbreaks.

BACKGROUND: Infestations with scabies mites are a global burden affecting individuals of all ages, classes and ethnicities. As poor sanitation and overcrowding favor the transmission of this highly contagious disease, epidemic outbreaks are frequently observed among displaced persons and asylum seekers. Due to the growing influx of refugees during the last years, public health authorities in host countries are frequently confronted with the challenge to treat individuals with diagnosed or suspected scabies promptly and effectively to avoid further spreading of the infestation. This study aimed to establish a straightforward and efficient algorithm for rapid screening and treatment of large numbers of patients with confirmed or suspected scabies infestations. METHODS: Forty-eight individuals (58% males, mean age 22.4 yrs.) from Syria with suspected scabies infestation were allocated to 3 colour-coded groups: (1) no signs or symptoms of infestation, (2) itch only, and (3) itch and typical skin lesions. Patients were treated with a single (group 1) or two doses of oral ivermectin at an interval of 7 days (group 2), or with a combination of 2 doses of ivermectin plus 2 applications of permethrin ointment at an interval of 7 days (group 3). Follow-ups were performed 4 weeks after initial treatments. RESULTS: All individuals with signs and/or symptoms of infestation had improved skin lesion; in 10/11 (90.9%) lesion had completely resolved. All individuals with initial itch only (n = 32) reported improvement of its intensity or complete resolution. None of the patients of group 1 developed itch or skin lesions. The algorithm was reapplied in 4 individuals (8.3%) after 4 weeks and the outbreak was completely controlled after 8 weeks. Colour-coding ensured fast flow of information between health-care providers at the interfaces of the algorithm. CONCLUSIONS: Our algorithm proved to be both highly efficient for treatment of large numbers of patients with suspected or diagnosed scabies infestation as well as for prevention of spreading of the disease. Hence, this algorithm is well suited for the management of scabies mass outbreaks.

HIV transmission at a Saudi Arabia hemodialysis unit.

BACKGROUND: Hemodialysis is associated with increased risk of healthcare-associated infections but considered a low-risk setting for human immunodeficiency virus (HIV) transmission. We investigated 3 hemodialysis unit (HDU) patients with new HIV infections to determine whether transmission was hemodialysis-associated and to correct factors that contributed to transmission. METHODS: Each patient was evaluated for HIV risk factors. Blood samples were tested to determine relatedness of HIV strains. Clinical data (gathered over 18 months) was reviewed to identify seroconversions at 12 HDUs. Infection prevention and control practices were evaluated at 14 HDUs. FINDINGS: No other HIV seroconversions were identified during the study. HIV gag, pol, and env gene sequences were consistent with a clonal relationship. HIV and hepatitis C virus prevalence rates at one HDU 1 (5.7% and 6.5%, respectively) were higher than for 11 other HDUs (0% and 0.15%, respectively). CONCLUSIONS: Sequencing supports either patient-to-patient or common-source transmission. Infections occurred despite Saudi Arabia's low HIV prevalence and national dialysis policies that emphasize stringent infection prevention and control practices.

Gaidai reliability method for high-dimensional spatio-temporal biosystems.

This study presents novel methodology for pandemic risks assessment for a national health system of interest. The 2019 coronavirus disease (COVID-19) is a contagious disease with certain potential for worldwide spread and potentially significant effects on public health globally. Suggested methodology enables risks assessment of an epidemic, that may happen in the near future at any time, and in any national region of interest. Traditional spatio-temporal reliability methodologies do not have benefit of easily handling health system's high-dimensionality and complex cross-correlations between regional observations. Contrarily, advocated Gaidaireliability approach successfully addresses spatiotemporal clinical observations, as well as multi-regional epidemiological dynamics. This study aimed at benchmarking of a novel bio-statistical technique, enabling national health risk assessment, based on available clinical surveys with dynamically observed patient numbers, while accounting for relevant territorial mappings. The method developed in this study opens up the possibility of accurate epidemiological risk forecast for multi-regional biological and health systems. Suggested bioinformatical methodology may be used in a wide range of public health applications.

Myroides odoratimimus: a New Threat with Persistent Infections, Multidrug Resistance, and the Potential for Hospital Outbreaks.

Myroides species have recently been reported more frequently in outbreaks in clinics and intensive care units (ICUs). In this study, we aimed to investigate the epidemic potential, antibiotic resistance profile, and risk factors of M. odoratimimus isolates that are increasingly being isolated from the ICUs of our hospital. Data from patients whose Myroides spp. were isolated from their clinical specimens over a 5-year period (September 2016 to January 2022) were retrospectively analyzed. Bacterial identification was performed using a matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). The presence of antibiotic resistance genes was analyzed using polymerase chain reaction (PCR). Possible clonal associations between isolates were investigated using enterobacterial repetitive intergenic consensus (ERIC)-PCR. As a result, 66 isolates were identified as M. odoratimimus and one isolate was identified as M. odoratus. The blaMUS resistance gene was detected in all M. odoratimimus isolates, whereas sul2 was detected in ten isolates and tetX was detected in 11 isolates. No other resistance genes, such as blaTUS, were detected. Additionally, two different clonal association patterns were discovered in the 24 selected isolates through the ERIC-PCR method. The increase in the immunosuppressive patient population indicate the possibility of encountering this agent and other opportunistic pathogens more frequently in the future.

Multi-regional COVID-19 epidemic forecast in Sweden.

The novel coronavirus disease 2019 (COVID-19) is a contagious disease with high transmissibility to spread worldwide, reported to present a certain burden on worldwide public health. This study aimed to determine epidemic occurrence probability at any reasonable time horizon in any region of interest by applying modern novel statistical methods directly to raw clinical data. This paper describes a novel bio-system reliability approach, particularly suitable for multi-regional health and stationary environmental systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of the highly pathogenic virus outbreak probability. For this study, COVID-19 daily recorded patient numbers in most affected Sweden regions were chosen. This work aims to benchmark state-of-the-art methods, making it possible to extract necessary information from dynamically observed patient numbers while considering relevant territorial mapping. The method proposed in this paper opens up the possibility of accurately predicting epidemic outbreak probability for multi-regional biological systems. Based on their clinical survey data, the suggested methodology can be used in various public health applications. Key findings are: A novel spatiotemporal health system reliability method has been developed and applied to COVID-19 epidemic data.Accurate multi-regional epidemic occurrence prediction is made.Epidemic threshold confidence bands given.

Epidemic history investigation: a new method of finding close contacts.

Introduction: Coronavirus disease 2019 has become a major global public health concern in December 2019. However, finding and excluding close contacts of COVID-19 infectors is a critical but difficult issue. This study aimed to introduce a new method of epidemiological investigation named space-time companions, which was adopted in Chengdu, China, in November 2021. Methods: An observational investigation was conducted during a small outbreak of COVID-19 in Chengdu, China in November 2021. A new method of epidemiological investigation called space-time companion was adopted in this outbreak, which was defined as the one who stayed in the same spatiotemporal grid (range: 800 m * 800 m) with the confirmed COVID-19 infector for more than 10 min in the last 14 days. A flow chart was used to describe the screening process of space-time companions in detail and illustrate the space-time companion epidemic management method. Results: The COVID-19 epidemic outbreak in Chengdu was effectively controlled for approximately one incubation period (14 days). After four rounds of space-time companions screening, more than 450,000 space-time companions were screened, including 27 COVID-19 infectors. Moreover, in the subsequent rounds of nucleic acid testing for all people in the city, no infected person were found proving the end of this epidemic outbreak. Conclusion: The space-time companion provides a new idea for screening close contacts of the COVID-19 infector and other similar infectious diseases, which can serve as a supplement to traditional epidemiological history surveys to verify and avoid missing close contacts.

COVID-19 Epidemic Forecast in Brazil.

This study advocates a novel spatio-temporal method for accurate prediction of COVID-19 epidemic occurrence probability at any time in any Brazil state of interest, and raw clinical observational data have been used. This article describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient time period, resulting in robust long-term forecast of the virus outbreak probability. COVID-19 daily numbers of recorded patients in all affected Brazil states were taken into account. This work aimed to benchmark novel state-of-the-art methods, making it possible to analyse dynamically observed patient numbers while taking into account relevant regional mapping. Advocated approach may help to monitor and predict possible future epidemic outbreaks within a large variety of multi-regional biological systems. Suggested methodology may be used in various modern public health applications, efficiently using their clinical survey data.

How heterogeneity in density dependence affects disease spread: when lifestyle matters.

People's lifestyles play a major role in disease risk. Some employment sectors and transport modes involve fixed exposures regardless of community size, while in other settings exposure tracks with population density. MERS-CoV, a coronavirus discovered in Saudi Arabia in 2012 closely related to those causing SARS and COVID-19, appears to need extended contact time for transmission, making some segments of a community at greater risk than others. We model mathematically how heterogeneity in contact rate structure impacts disease spread, using as a case study a MERS outbreak in two Saudi Arabian communities. We divide the at-risk population into segments with exposure rates either independent of population density or density-dependent. Analysis shows disease spread is minimized for intermediate size populations with a limited proportion of individuals in the density-independent group. In the case study, the high proportion of density-independent exposure may explain the historical outbreak's extinction in the larger city.

COVID-19 spatio-temporal forecast in England.

The 2019 novel coronavirus disease (COVID-19, SARS-CoV-2) being contagious illness with allegedly high potential for global transmission, low potential for morbidity and fatality, and certain impact on global public health. This study describes a novel bio-system reliability spatio-temporal approach, that is especially appropriate for multi-regional environmental, biological and health systems and that, when observed for a sufficient amount of time, produces a reliable long-term forecast of the likelihood of an outbreak of a highly pathogenic virus. Conventional statistical approaches do not have the benefit of effectively handling large regional dimensionality and cross-correlation between various regional observations. These methods deal with temporal observations of multi-regional phenomena. The most afflicted districts of England's COVID-19 daily counts of reported patients were used for this investigation. In order to extract the essential data from dynamically observed patient numbers while taking into consideration pertinent geographical mapping, this study utilized recently developed bio-reliability methodology. With the use of the spatio-temporal approach described in this study, future epidemic outbreak risks for multi-regional public health systems may be predicted with sufficient accuracy.

A MATLAB toolbox to fit and forecast growth trajectories using phenomenological growth models: Application to epidemic outbreaks.

Background: Simple dynamic modeling tools can be useful for generating real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for this purpose is lacking. Results: In this tutorial-based primer, we introduce and illustrate a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using phenomenological dynamic growth models based on ordinary differential equations. This toolbox is accessible to various audiences, including students training in time-series forecasting, dynamic growth modeling, parameter estimation, parameter uncertainty and identifiability, model comparison, performance metrics, and forecast evaluation, as well as researchers and policymakers who need to conduct short-term forecasts in real-time. The models included in the toolbox capture exponential and sub-exponential growth patterns that typically follow a rising pattern followed by a decline phase, a common feature of contagion processes. Models include the 2-parameter generalized-growth model, which has proved useful to characterize and forecast the ascending phase of epidemic outbreaks, and the Gompertz model as well as the 3-parameter generalized logistic-growth model and the Richards model, which have demonstrated competitive performance in forecasting single peak outbreaks.The toolbox provides a tutorial for forecasting time-series trajectories that include the full uncertainty distribution, derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance across different models, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. Conclusions: We have developed the first comprehensive toolbox to characterize and forecast time-series data using simple phenomenological growth models. As a contagion process takes off, the tools presented in this tutorial can facilitate policymaking to guide the implementation of control strategies and assess the impact of interventions. The toolbox functionality is demonstrated through various examples, including a tutorial video, and is illustrated using weekly data on the monkeypox epidemic in the USA.

Gaidai reliability method for long-term coronavirus modelling.

Background: Novel coronavirus disease has been recently a concern for worldwide public health. To determine epidemic rate probability at any time in any region of interest, one needs efficient bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the multi-dimensionality advantage, that suggested methodology offers, namely dealing efficiently with multiple regions at the same time and accounting for cross-correlations between different regional observations. Methods: Modern multi-dimensional novel statistical method was directly applied to raw clinical data, able to deal with territorial mapping. Novel reliability method based on statistical extreme value theory has been suggested to deal with challenging epidemic forecast. Authors used MATLAB optimization software. Results: This paper described a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. Namely, accurate maximum recorded patient numbers are predicted for the years to come for the analyzed provinces. Conclusions: The suggested method performed well by supplying not only an estimate but 95% confidence interval as well. Note that suggested methodology is not limited to any specific epidemics or any specific terrain, namely its truly general. The only assumption and limitation is bio-system stationarity, alternatively trend analysis should be performed first. The suggested methodology can be used in various public health applications, based on their clinical survey data.

Spatial organisation plasticity reduces disease infection risk in rock-paper-scissors models.

We study a three-species cyclic game system where organisms face a contagious disease whose virulence may change by a pathogen mutation. As a responsive defence strategy, organisms' mobility is restricted to reduce disease dissemination in the system. The impact of the collective self-preservation strategy on the disease infection risk is investigated by performing stochastic simulations of the spatial version of the rock-paper-scissors game. Our outcomes show that the mobility control strategy induces plasticity in the spatial patterns with groups of organisms of the same species inhabiting spatial domains whose characteristic length scales depend on the level of dispersal restrictions. The spatial organisation plasticity allows the ecosystems to adapt to minimise the individuals' disease contamination risk if an eventual pathogen alters the disease virulence. We discover that if a pathogen mutation makes the disease more transmissible or less lethal, the organisms benefit more if the mobility is not strongly restricted, thus forming large spatial domains. Conversely, the benefits of protecting against a pathogen causing a less contagious or deadlier disease are maximised if the average size of groups of individuals of the same species is significantly limited, reducing the dimensions of groups of organisms significantly. Our findings may help biologists understand the effects of dispersal control as a conservation strategy in ecosystems affected by epidemic outbreaks.