Abstract Objectives The excessive spread of the pandemic COVID-19 around the globe has put mankind at risk. The medical infrastructure and resources are frazzled, even for the world's top economies, due to the large COVID-19 infection. To cope up with this situation, countries are exploring the pool test strategies. In this paper, a detailed analysis has been done to explore the efficient pooling strategies. Given a population and the known fact that the percentage of people infected by the virus, the minimum number of tests to identify COVID-19 positive cases from the entire population are found. In this paper, the problem is formulated with an objective to find a minimum number of tests in the worst case where exactly one positive sample is there in a pool which can happen considering the fact that the groups are formed by choosing samples randomly. Therefore, the thrust stress is on minimizing the total number of tests by finding varying pool sizes at different levels (not necessarily same size at all levels), although levels can also be controlled. Methods Initially the problem is formulated as an optimization problem and there is no constraint on the number of levels upto which pooling can be done. Finding an analytical solution of the problem was challenging and thus the approximate solution was obtained and analyzed. Further, it is observed that many times it is pertinent to put a constraint on the number of levels upto which pooling can be done and thus optimizing with such a constraint is also done using genetic algorithm. Results An empirical evaluation on both realistic and synthetic examples is done to show the efficiency of the procedures and for lower values of percentage infection, the total number of tests are very much less than the population size. Further, the findings of this study show that the general COVID-19 pool test gives the better solution for a small infection while as the value of infection becomes significant the single COVID-19 pool test gives better results. Conclusions This paper illustrates the formation and analysis of polling strategies, which can be opted for the better utilization of the resources. Two different pooling strategies are proposed and these strategies yield accurate insight considering the worst case scenario. The analysis finds that the proposed bounds can be efficiently exploited to ascertain the pool testing in view of the COVID-19 infection rate.
{"title":"Mathematical formation and analysis of COVID-19 pool tests strategies","authors":"Sushmita Chandel, Gaurav Bhatnagar, Krishna Pratap Singh","doi":"10.21203/rs.3.rs-87411/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-87411/v1","url":null,"abstract":"Abstract Objectives The excessive spread of the pandemic COVID-19 around the globe has put mankind at risk. The medical infrastructure and resources are frazzled, even for the world's top economies, due to the large COVID-19 infection. To cope up with this situation, countries are exploring the pool test strategies. In this paper, a detailed analysis has been done to explore the efficient pooling strategies. Given a population and the known fact that the percentage of people infected by the virus, the minimum number of tests to identify COVID-19 positive cases from the entire population are found. In this paper, the problem is formulated with an objective to find a minimum number of tests in the worst case where exactly one positive sample is there in a pool which can happen considering the fact that the groups are formed by choosing samples randomly. Therefore, the thrust stress is on minimizing the total number of tests by finding varying pool sizes at different levels (not necessarily same size at all levels), although levels can also be controlled. Methods Initially the problem is formulated as an optimization problem and there is no constraint on the number of levels upto which pooling can be done. Finding an analytical solution of the problem was challenging and thus the approximate solution was obtained and analyzed. Further, it is observed that many times it is pertinent to put a constraint on the number of levels upto which pooling can be done and thus optimizing with such a constraint is also done using genetic algorithm. Results An empirical evaluation on both realistic and synthetic examples is done to show the efficiency of the procedures and for lower values of percentage infection, the total number of tests are very much less than the population size. Further, the findings of this study show that the general COVID-19 pool test gives the better solution for a small infection while as the value of infection becomes significant the single COVID-19 pool test gives better results. Conclusions This paper illustrates the formation and analysis of polling strategies, which can be opted for the better utilization of the resources. Two different pooling strategies are proposed and these strategies yield accurate insight considering the worst case scenario. The analysis finds that the proposed bounds can be efficiently exploited to ascertain the pool testing in view of the COVID-19 infection rate.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86827789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Objectives: Diseases such as SARS-CoV-2 have novel features that require modifications to the standard network-based stochastic SEIR model. In particular, we introduce modifications to this model to account for the potential changes in behavior patterns of individuals upon becoming symptomatic, as well as the tendency of a substantial proportion of those infected to remain asymptomatic. Methods: Using a generic network model where every potential contact exists with the same common probability, we conduct a simulation study in which we vary four key model parameters (transmission rate, probability of remaining asymptomatic, and the mean lengths of time spent in the exposed and infectious disease states) and examine the resulting impacts on various metrics of epidemic severity, including the effective reproduction number. We then consider the effects of a more complex network model. Results: We find that the mean length of time spent in the infectious state and the transmission rate are the most important model parameters, while the mean length of time spent in the exposed state and the probability of remaining asymptomatic are less important. We also find that the network structure has a significant impact on the dynamics of the disease spread. Conclusions: In this article, we present a modification to the network-based stochastic SEIR epidemic model which allows for modifications to the underlying contact network to account for the effects of quarantine. We also discuss the changes needed to the model to incorporate situations where some proportion of the individuals who are infected remain asymptomatic throughout the course of the disease.
{"title":"Modifying the network-based stochastic SEIR model to account for quarantine: an application to COVID-19","authors":"Chris Groendyke, Adam Combs","doi":"10.1515/em-2020-0030","DOIUrl":"https://doi.org/10.1515/em-2020-0030","url":null,"abstract":"Abstract Objectives: Diseases such as SARS-CoV-2 have novel features that require modifications to the standard network-based stochastic SEIR model. In particular, we introduce modifications to this model to account for the potential changes in behavior patterns of individuals upon becoming symptomatic, as well as the tendency of a substantial proportion of those infected to remain asymptomatic. Methods: Using a generic network model where every potential contact exists with the same common probability, we conduct a simulation study in which we vary four key model parameters (transmission rate, probability of remaining asymptomatic, and the mean lengths of time spent in the exposed and infectious disease states) and examine the resulting impacts on various metrics of epidemic severity, including the effective reproduction number. We then consider the effects of a more complex network model. Results: We find that the mean length of time spent in the infectious state and the transmission rate are the most important model parameters, while the mean length of time spent in the exposed state and the probability of remaining asymptomatic are less important. We also find that the network structure has a significant impact on the dynamics of the disease spread. Conclusions: In this article, we present a modification to the network-based stochastic SEIR epidemic model which allows for modifications to the underlying contact network to account for the effects of quarantine. We also discuss the changes needed to the model to incorporate situations where some proportion of the individuals who are infected remain asymptomatic throughout the course of the disease.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77782661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Nair, Rahul Thekkedath, Paduthol Godan Sankaran
Abstract Objectives Meteorological factors and climatic variability have an immense influence on the transmission of infectious diseases and significantly impact human health. Present study quantifies the delayed effect of atmospheric temperature on the risk of hospitalization due to the Coronavirus disease 2019 (COVID-19) with adjusting the effects of other environmental factors in Mumbai, India. Methods The daily reported data of the number of hospitalized COVID-19 positive cases and the environmental factors at Mumbai, Maharashtra, India were collected and analyzed to quantify the main and the delayed effects. Exploratory data analysis and Distributed Linear and Non-linear lag Model (DLNM) with Generalized Additive Model (GAM) specification have applied to analyze the data. Results The study identified the Diurnal Temperature Range (DTR) delayed effect on the risk of hospitalization changed over the lag period of 0–14 days with increasing Relative Risk (RR) at the low DTR and decreasing RR at the higher DTR values. The extreme DTR suggests a high risk of hospitalization at earlier lags (i.e., 0–5 days). DTR’s cumulative effect was significant at higher 0–10 lag days (p-value <0.05). Exposure to the low and moderate DTR suggests a high risk of hospitalization with more than six days of lag. The RR for daily average humidity with 95% C.I was 0.996 (0.967, 1.027). The risk of hospitalization due to COVID-19 showed an increasing nature (p-value <0.05) with the increase in air pollution and average wind speed (WSAvg) at lag 0. Also, the risk of hospitalization changed through different lag periods of DTR. The analysis confirms the higher amount of delayed effect due to low DTR compared with moderate and high DTR. Conclusions The study suggests that both the climatic variations and air quality have significant impact on the transmission of the global pandemic COVID-19.
{"title":"The delayed effect of temperature on the risk of hospitalization due to COVID-19: evidence from Mumbai, India","authors":"V. Nair, Rahul Thekkedath, Paduthol Godan Sankaran","doi":"10.1515/em-2020-0039","DOIUrl":"https://doi.org/10.1515/em-2020-0039","url":null,"abstract":"Abstract Objectives Meteorological factors and climatic variability have an immense influence on the transmission of infectious diseases and significantly impact human health. Present study quantifies the delayed effect of atmospheric temperature on the risk of hospitalization due to the Coronavirus disease 2019 (COVID-19) with adjusting the effects of other environmental factors in Mumbai, India. Methods The daily reported data of the number of hospitalized COVID-19 positive cases and the environmental factors at Mumbai, Maharashtra, India were collected and analyzed to quantify the main and the delayed effects. Exploratory data analysis and Distributed Linear and Non-linear lag Model (DLNM) with Generalized Additive Model (GAM) specification have applied to analyze the data. Results The study identified the Diurnal Temperature Range (DTR) delayed effect on the risk of hospitalization changed over the lag period of 0–14 days with increasing Relative Risk (RR) at the low DTR and decreasing RR at the higher DTR values. The extreme DTR suggests a high risk of hospitalization at earlier lags (i.e., 0–5 days). DTR’s cumulative effect was significant at higher 0–10 lag days (p-value <0.05). Exposure to the low and moderate DTR suggests a high risk of hospitalization with more than six days of lag. The RR for daily average humidity with 95% C.I was 0.996 (0.967, 1.027). The risk of hospitalization due to COVID-19 showed an increasing nature (p-value <0.05) with the increase in air pollution and average wind speed (WSAvg) at lag 0. Also, the risk of hospitalization changed through different lag periods of DTR. The analysis confirms the higher amount of delayed effect due to low DTR compared with moderate and high DTR. Conclusions The study suggests that both the climatic variations and air quality have significant impact on the transmission of the global pandemic COVID-19.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86006464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In 1854,Dr. JohnSnow laid the foundations of epidemiologyby applying statistical thinking to the investigation of the cholera epidemic in London, but also by acting on it despite the great uncertainty that reigned (Snow 1856). This is a tale known to all epidemiology students, the prevailing theory ofwhichwas that, at the time, cholerawas caused by miasmas – bad smells. Snow carried out the first statistical study, which one would qualify today as “ecological”. He observed that cholera occurred more often among people living in buildings with higher proportions of subscribers to awater pumpdrawing itswater downstreamof a river-borne sewage spill in the Thames, compared to those subscribed to apumpdrawing itswater upstreamof sucha landfill. He thencarriedout a study, the equivalent to a “case-control study” as we called them nowadays, comparing cholera patients to otherwise healthy people (non-cholera sample) at an individual level and checked which pump they were subscribed to precisely. Upon calculating the “odds ratio” that played against the downstreampump, he concluded that cholera wasprobably transmitted through consumptionof sewage-contaminatedwater. Despite his innovative reasoning, Snow did not succeed in convincing his contemporary peers with mere statistics. Of a decisive character – a reputed obstetrician he twice assisted Queen Victoria through childbirth with experimental anesthesia – he removed the handle of the incriminated pump himself, rendering it ineffective. The cholera epidemic resolved soon after. It is only almost 30 years later that Robert Koch convincingly demonstrated that a vibrio, first isolated by Filippo Pacini in 1854, caused the disease (Bentivoglio and Pacini 1995; Howard-Jones 1984). Yet, Snow had demonstrated statistically and empirically, bymeans of action, that the pumpwas the real cause of theproblemat hand, the epidemic. One can draw from this experience that sound epidemiology may be as powerful as microbiology at identifying determinants of diseases when what it actually showed was that epidemiology is good at finding causes of epidemics, without needing to even know the cause of the disease itself. The biggest lesson, in fact, is however often forgotten: the importance of acting under uncertainty and that epidemiology is a science of probability with no real impact if not followed by action. Indeed, a large number of epidemiologists have since become exactly the opposite of what Snow demonstrated. Becoming specialists in identifying uncertainty in any scientific endeavor, epidemiologycanoftenput thebrakesonaction. From this perspective, theunfoldingaccount of the COVID-19 epidemic is deeply instructive. On December 30, 2019, two days after being admitted to hospital with respiratory symptoms, a first case of a so-called “coronavirus-SARS” was diagnosed in Wuhan, known today as the epicenter of the COVID-19 pandemic (Report of the WHO 2020). Launched by the emergency department at Wuhan Central Hospital, t
1854年,博士。约翰·斯诺(JohnSnow)将统计思维应用于伦敦霍乱疫情的调查，并在当时存在巨大不确定性的情况下采取行动，为流行病学奠定了基础(斯诺，1856)。这是一个所有流行病学学生都知道的故事，当时流行的理论是，霍乱是由瘴气——难闻的气味——引起的。斯诺进行了第一次统计研究，今天可以称之为“生态学”。他观察到，与那些从垃圾填埋场上游抽水的人相比，那些从泰晤士河的河流污水溢出处抽水的人比例更高的建筑物中，霍乱更常发生在那些居住在那里的人身上。然后，他进行了一项研究，相当于我们现在所说的“病例对照研究”，将霍乱患者与其他健康人群(非霍乱样本)在个人水平上进行比较，并检查他们精确地订阅了哪个泵。在计算了与下游水泵相反的“比值比”后，他得出结论:霍乱可能是通过饮用被污水污染的水传播的。尽管斯诺的推理具有创新性，但他并没有成功地用统计数据说服同时代的同行。作为一名著名的产科医生，他曾两次在实验性麻醉下帮助维多利亚女王分娩，他果断地拔掉了被指控的泵的把手，使其失效。霍乱疫情很快就平息了。直到近30年后，罗伯特·科赫才令人信服地证明，1854年由菲利波·帕西尼首次分离出的弧菌导致了这种疾病(Bentivoglio and Pacini 1995;howard jones 1984)。然而，斯诺通过实际行动，从统计数据和经验上证明，水泵才是问题——流行病——的真正原因。人们可以从这一经验中得出结论，在确定疾病的决定因素方面，合理的流行病学可能与微生物学一样强大，而它实际上表明，流行病学善于发现流行病的原因，甚至不需要知道疾病本身的原因。然而，最大的教训实际上却经常被遗忘:在不确定的情况下采取行动的重要性，流行病学是一门概率科学，如果不采取行动，就不会产生真正的影响。事实上，从那以后，大量流行病学家的观点与斯诺的观点完全相反。在任何科学研究中，当流行病学成为识别不确定性的专家时，他们往往无法阻止这种反应。从这个角度来看，对新冠肺炎疫情的描述极具启发性。2019年12月30日，在因呼吸道症状入院两天后，武汉确诊了第一例所谓的“冠状病毒- sars”病例，武汉今天被称为COVID-19大流行的中心(世界卫生组织2020年报告)。武汉市中心医院急诊科发布的第一个警报被一名检查人员拒绝，该检查人员指示医生不要出声，以免引起警报
{"title":"New coronavirus pandemic: an analysis paralysis?","authors":"L. Abenhaim","doi":"10.1515/em-2020-0006","DOIUrl":"https://doi.org/10.1515/em-2020-0006","url":null,"abstract":"In 1854,Dr. JohnSnow laid the foundations of epidemiologyby applying statistical thinking to the investigation of the cholera epidemic in London, but also by acting on it despite the great uncertainty that reigned (Snow 1856). This is a tale known to all epidemiology students, the prevailing theory ofwhichwas that, at the time, cholerawas caused by miasmas – bad smells. Snow carried out the first statistical study, which one would qualify today as “ecological”. He observed that cholera occurred more often among people living in buildings with higher proportions of subscribers to awater pumpdrawing itswater downstreamof a river-borne sewage spill in the Thames, compared to those subscribed to apumpdrawing itswater upstreamof sucha landfill. He thencarriedout a study, the equivalent to a “case-control study” as we called them nowadays, comparing cholera patients to otherwise healthy people (non-cholera sample) at an individual level and checked which pump they were subscribed to precisely. Upon calculating the “odds ratio” that played against the downstreampump, he concluded that cholera wasprobably transmitted through consumptionof sewage-contaminatedwater. Despite his innovative reasoning, Snow did not succeed in convincing his contemporary peers with mere statistics. Of a decisive character – a reputed obstetrician he twice assisted Queen Victoria through childbirth with experimental anesthesia – he removed the handle of the incriminated pump himself, rendering it ineffective. The cholera epidemic resolved soon after. It is only almost 30 years later that Robert Koch convincingly demonstrated that a vibrio, first isolated by Filippo Pacini in 1854, caused the disease (Bentivoglio and Pacini 1995; Howard-Jones 1984). Yet, Snow had demonstrated statistically and empirically, bymeans of action, that the pumpwas the real cause of theproblemat hand, the epidemic. One can draw from this experience that sound epidemiology may be as powerful as microbiology at identifying determinants of diseases when what it actually showed was that epidemiology is good at finding causes of epidemics, without needing to even know the cause of the disease itself. The biggest lesson, in fact, is however often forgotten: the importance of acting under uncertainty and that epidemiology is a science of probability with no real impact if not followed by action. Indeed, a large number of epidemiologists have since become exactly the opposite of what Snow demonstrated. Becoming specialists in identifying uncertainty in any scientific endeavor, epidemiologycanoftenput thebrakesonaction. From this perspective, theunfoldingaccount of the COVID-19 epidemic is deeply instructive. On December 30, 2019, two days after being admitted to hospital with respiratory symptoms, a first case of a so-called “coronavirus-SARS” was diagnosed in Wuhan, known today as the epicenter of the COVID-19 pandemic (Report of the WHO 2020). Launched by the emergency department at Wuhan Central Hospital, t","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90454460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Irene Rocchetti, D. Böhning, H. Holling, A. Maruotti
Abstract Background While the number of detected COVID-19 infections are widely available, an understanding of the extent of undetected cases is urgently needed for an effective tackling of the pandemic. The aim of this work is to estimate the true number of COVID-19 (detected and undetected) infections in several European countries. The question being asked is: How many cases have actually occurred? Methods We propose an upper bound estimator under cumulative data distributions, in an open population, based on a day-wise estimator that allows for heterogeneity. The estimator is data-driven and can be easily computed from the distributions of daily cases and deaths. Uncertainty surrounding the estimates is obtained using bootstrap methods. Results We focus on the ratio of the total estimated cases to the observed cases at April 17th. Differences arise at the country level, and we get estimates ranging from the 3.93 times of Norway to the 7.94 times of France. Accurate estimates are obtained, as bootstrap-based intervals are rather narrow. Conclusions Many parametric or semi-parametric models have been developed to estimate the population size from aggregated counts leading to an approximation of the missed population and/or to the estimate of the threshold under which the number of missed people cannot fall (i.e. a lower bound). Here, we provide a methodological contribution introducing an upper bound estimator and provide reliable estimates on the dark number, i.e. how many undetected cases are going around for several European countries, where the epidemic spreads differently.
{"title":"Estimating the size of undetected cases of the COVID-19 outbreak in Europe: an upper bound estimator","authors":"Irene Rocchetti, D. Böhning, H. Holling, A. Maruotti","doi":"10.1515/em-2020-0024","DOIUrl":"https://doi.org/10.1515/em-2020-0024","url":null,"abstract":"Abstract Background While the number of detected COVID-19 infections are widely available, an understanding of the extent of undetected cases is urgently needed for an effective tackling of the pandemic. The aim of this work is to estimate the true number of COVID-19 (detected and undetected) infections in several European countries. The question being asked is: How many cases have actually occurred? Methods We propose an upper bound estimator under cumulative data distributions, in an open population, based on a day-wise estimator that allows for heterogeneity. The estimator is data-driven and can be easily computed from the distributions of daily cases and deaths. Uncertainty surrounding the estimates is obtained using bootstrap methods. Results We focus on the ratio of the total estimated cases to the observed cases at April 17th. Differences arise at the country level, and we get estimates ranging from the 3.93 times of Norway to the 7.94 times of France. Accurate estimates are obtained, as bootstrap-based intervals are rather narrow. Conclusions Many parametric or semi-parametric models have been developed to estimate the population size from aggregated counts leading to an approximation of the missed population and/or to the estimate of the threshold under which the number of missed people cannot fall (i.e. a lower bound). Here, we provide a methodological contribution introducing an upper bound estimator and provide reliable estimates on the dark number, i.e. how many undetected cases are going around for several European countries, where the epidemic spreads differently.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75942201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Fiorillo, M. Cicciu', Rosa De Stefano, S. Bocchieri, A. Herford, M. Fazio, G. Cervino
Abstract The digital field certainly provides a lot of information in the medical field, it is possible, in a computerized way, also to simulate epidemics, and the spread of these. There have been events in the past, in some simulation games, which are currently being studied, as they could provide important clues for the resolution of epidemics such as the one from COVID-19. One of these events occurred due to a bug in 2005 in the role-playing online game World of Warcraft. Through these simulations it is possible to make prophylactic plans to intervene preventively or plan interventions throughout mathematical models.
{"title":"Virtual reality and massive multiplayer online role-playing games as possible prophylaxis mathematical model: focus on COVID-19 spreading","authors":"L. Fiorillo, M. Cicciu', Rosa De Stefano, S. Bocchieri, A. Herford, M. Fazio, G. Cervino","doi":"10.1515/em-2020-0003","DOIUrl":"https://doi.org/10.1515/em-2020-0003","url":null,"abstract":"Abstract The digital field certainly provides a lot of information in the medical field, it is possible, in a computerized way, also to simulate epidemics, and the spread of these. There have been events in the past, in some simulation games, which are currently being studied, as they could provide important clues for the resolution of epidemics such as the one from COVID-19. One of these events occurred due to a bug in 2005 in the role-playing online game World of Warcraft. Through these simulations it is possible to make prophylactic plans to intervene preventively or plan interventions throughout mathematical models.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79850149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Introduction Time series models are one of the frequently used methods to describe the pattern of spreading an epidemic. Methods We presented a new family of time series models able to represent the cumulative number of individuals that contracted an infectious disease from the start to the end of the first wave of spreading. This family is flexible enough to model the propagation of almost all infectious diseases. After a general discussion on competent time series to model the outbreak of a communicable disease, we introduced the new family through one of its examples. Results We estimated the parameters of two samples of the novel family to model the spreading of COVID-19 in China. Discussion Our model does not work well when the decreasing trend of the rate of growth is absent because it is the main presumption of the model. In addition, since the information on the initial days is of the utmost importance for this model, one of the challenges about this model is modifying it to get qualified to model datasets that lack the information on the first days.
{"title":"Modelling spreading of an infection using time series by a novel family of models; fitting the time series of the confirmed cases of COVID-19 in China","authors":"B. Jamshidi, S. Jamshidi Zargaran, M. Rezaei","doi":"10.1515/em-2020-0013","DOIUrl":"https://doi.org/10.1515/em-2020-0013","url":null,"abstract":"Abstract Introduction Time series models are one of the frequently used methods to describe the pattern of spreading an epidemic. Methods We presented a new family of time series models able to represent the cumulative number of individuals that contracted an infectious disease from the start to the end of the first wave of spreading. This family is flexible enough to model the propagation of almost all infectious diseases. After a general discussion on competent time series to model the outbreak of a communicable disease, we introduced the new family through one of its examples. Results We estimated the parameters of two samples of the novel family to model the spreading of COVID-19 in China. Discussion Our model does not work well when the decreasing trend of the rate of growth is absent because it is the main presumption of the model. In addition, since the information on the initial days is of the utmost importance for this model, one of the challenges about this model is modifying it to get qualified to model datasets that lack the information on the first days.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86007239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Objective This paper is establishing the relationship between the spreading dynamics of the Covid-19 pandemic in Morocco and the efficiency of the measures and actions taken by public authorities to contain it. The main objective is to predict the evolution of the COVID-19 pandemic in Morocco and to estimate the time needed for its disappearance. Methods For these reasons, we have highlighted the role of mathematical models in understanding the transmission chain of this virus as well as its future evolution. Then we used the SIR epidemiological model, which proves to be well suited to address this issue. It shows that identification of the key parameters of this pandemic, such as the probability of transmission, should help to adequately explain its behaviour and make it easier to predict its progress. Results As a result, the measures and actions taken by the public authorities in Morocco allowed to record lower number of virus reproduction than many countries. Conclusion So, in the case of Morocco, we were able to predict that the Covid-19 pandemic should disappear in a shorter time and without registering a larger number of infected individuals compared to other countries.
{"title":"Modeling the spread of Covid-19 pandemic: case of Morocco","authors":"Bilal Lotfi, Ismail Lotfi, Oussama Aoun","doi":"10.1515/em-2020-0004","DOIUrl":"https://doi.org/10.1515/em-2020-0004","url":null,"abstract":"Abstract Objective This paper is establishing the relationship between the spreading dynamics of the Covid-19 pandemic in Morocco and the efficiency of the measures and actions taken by public authorities to contain it. The main objective is to predict the evolution of the COVID-19 pandemic in Morocco and to estimate the time needed for its disappearance. Methods For these reasons, we have highlighted the role of mathematical models in understanding the transmission chain of this virus as well as its future evolution. Then we used the SIR epidemiological model, which proves to be well suited to address this issue. It shows that identification of the key parameters of this pandemic, such as the probability of transmission, should help to adequately explain its behaviour and make it easier to predict its progress. Results As a result, the measures and actions taken by the public authorities in Morocco allowed to record lower number of virus reproduction than many countries. Conclusion So, in the case of Morocco, we were able to predict that the Covid-19 pandemic should disappear in a shorter time and without registering a larger number of infected individuals compared to other countries.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74839346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper reports a broad study using epidemic-related counting data of COVID-19 disease caused by the novel coronavirus (SARS-CoV-2). The considered dataset refers to 119 countries’ daily counts of reported cases and deaths in a fixed period. For the data analysis, it has been adopted a beta regression model assuming different regions of the world where it was possible to discover important economic, health and social factors affecting the behavior of the pandemic in different countries. The Bayesian method was applied to fit the proposed model. Some interesting conclusions were obtained in this study, which could be of great interest to epidemiologists, health authorities, and the general public in the face of the forthcoming hard times of the global pandemic.
{"title":"Modeling the incidence and death rates of COVID-19 pandemic in different regions of the world","authors":"R. P. de Oliveira, J. Achcar, A. Nunes","doi":"10.1515/em-2020-0017","DOIUrl":"https://doi.org/10.1515/em-2020-0017","url":null,"abstract":"Abstract This paper reports a broad study using epidemic-related counting data of COVID-19 disease caused by the novel coronavirus (SARS-CoV-2). The considered dataset refers to 119 countries’ daily counts of reported cases and deaths in a fixed period. For the data analysis, it has been adopted a beta regression model assuming different regions of the world where it was possible to discover important economic, health and social factors affecting the behavior of the pandemic in different countries. The Bayesian method was applied to fit the proposed model. Some interesting conclusions were obtained in this study, which could be of great interest to epidemiologists, health authorities, and the general public in the face of the forthcoming hard times of the global pandemic.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88671009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Jamshidi, M. Rezaei, S. Jamshidi Zargaran, F. Najafi
Abstract In epidemiology, the modeling of epicenters is important both conceptually and mathematically. This paper is an attempt to model epicenters mathematically. We present an algorithm to find new epicenters. Applying our model for the data related to COVID-19 pandemic, we obtain epicenters in China, South Korea, Iran, Italy, France, Germany, Spain, the USA, and Switzerland, on the days 1, 35, 42, 42, 49, 50, 50, 50, and 56, respectively. Although the number of these epicenters is less than 5% of all contaminated countries across the globe, as of March 22, 2020, they make up 74% of new cases and over 80% of total confirmed cases. Finally, we conclude that we expect to face three new epicenters between March 22 and April 1, 2020.
{"title":"Mathematical modeling the epicenters of coronavirus disease-2019 (COVID-19) pandemic","authors":"B. Jamshidi, M. Rezaei, S. Jamshidi Zargaran, F. Najafi","doi":"10.1515/em-2020-0009","DOIUrl":"https://doi.org/10.1515/em-2020-0009","url":null,"abstract":"Abstract In epidemiology, the modeling of epicenters is important both conceptually and mathematically. This paper is an attempt to model epicenters mathematically. We present an algorithm to find new epicenters. Applying our model for the data related to COVID-19 pandemic, we obtain epicenters in China, South Korea, Iran, Italy, France, Germany, Spain, the USA, and Switzerland, on the days 1, 35, 42, 42, 49, 50, 50, 50, and 56, respectively. Although the number of these epicenters is less than 5% of all contaminated countries across the globe, as of March 22, 2020, they make up 74% of new cases and over 80% of total confirmed cases. Finally, we conclude that we expect to face three new epicenters between March 22 and April 1, 2020.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76636344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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