Abstract Objectives Analyzing the dynamics and patterns of birth intervals in the Algerian population is an important issue in developing an effective population policy. In this study, we attempted to estimate the effects of socioeconomic and demographic factors on the birth spacing process. Methods Semi-Markov models were used, based on data from the Multiple Indicator Cluster Survey (MICS), where the birth histories of 13,453 infants nested within a sample of 6,958 married women were analyzed. Results The findings stated that the birth intervals depend on: (i) mothers’ educational level, whereas wider intervals have been found for highly educated women, (ii) the wealth index, as women from poor families have short birth intervals, and (iii) there was no clear difference between rural and urban areas. Conclusions Policymakers can act through these axes to develop more efficient strategies for family planning.
{"title":"Determinants of birth-intervals in Algeria: a semi-Markov model analysis","authors":"F. Chellai","doi":"10.1515/em-2021-0030","DOIUrl":"https://doi.org/10.1515/em-2021-0030","url":null,"abstract":"Abstract Objectives Analyzing the dynamics and patterns of birth intervals in the Algerian population is an important issue in developing an effective population policy. In this study, we attempted to estimate the effects of socioeconomic and demographic factors on the birth spacing process. Methods Semi-Markov models were used, based on data from the Multiple Indicator Cluster Survey (MICS), where the birth histories of 13,453 infants nested within a sample of 6,958 married women were analyzed. Results The findings stated that the birth intervals depend on: (i) mothers’ educational level, whereas wider intervals have been found for highly educated women, (ii) the wealth index, as women from poor families have short birth intervals, and (iii) there was no clear difference between rural and urban areas. Conclusions Policymakers can act through these axes to develop more efficient strategies for family planning.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73665281","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 The Fetal–Infant mortality rate (FIMR) is the basic surveillance statistic in perinatal periods of risk (PPOR) analyses. This paper presents a model for the FIMR as the ratio of two Poisson random variables. From this model, expressions for estimators of variance, standard error, and relative standard error are developed. The coverage properties of interval estimators for the FIMR are investigated in a simulation study for both small and large populations and FIMR rates. Results from these studies are applied to a PPOR analysis of NC vital records. Results suggest that the sample size guidance provided in the literature to ensure statistical reliability is overly conservative and interval construction methodology should be selected based on population size.
{"title":"Reliability of fetal–infant mortality rates in perinatal periods of risk (PPOR) analysis","authors":"Vito L Di Bona","doi":"10.1515/em-2019-0026","DOIUrl":"https://doi.org/10.1515/em-2019-0026","url":null,"abstract":"Abstract The Fetal–Infant mortality rate (FIMR) is the basic surveillance statistic in perinatal periods of risk (PPOR) analyses. This paper presents a model for the FIMR as the ratio of two Poisson random variables. From this model, expressions for estimators of variance, standard error, and relative standard error are developed. The coverage properties of interval estimators for the FIMR are investigated in a simulation study for both small and large populations and FIMR rates. Results from these studies are applied to a PPOR analysis of NC vital records. Results suggest that the sample size guidance provided in the literature to ensure statistical reliability is overly conservative and interval construction methodology should be selected based on population size.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"125 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74930310","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}
K. M. J. Krishna, T. Traison, Sejil Mariya Sebastian, P. George, A. Mathew
Abstract Objectives: In time to event analysis, the risk for an event is usually estimated using Cox proportional hazards (CPH) model. But CPH model has the limitation of biased estimate due to unobserved hidden heterogeneity among the covariates, which can be tackled using frailty models. The best models were usually being identified using Akaike information criteria (AIC). Apart from AIC, the present study aimed to assess predictability of risk models using survival concordance measure. Methods: CPH model and frailty models were used to estimate the risk for breast cancer patient survival, and the frailty variable was assumed to follow gamma distribution. Schoenfeld global test was used to check the proportionality assumption. Survival concordance, AIC and simulation studies were used to identify the significance of frailty. Results: From the univariate analysis it was observed that for the covariate age, the frailty has a significant role (θ = 2.758, p-value: 0.0004) and the corresponding hazard rate was 1.93 compared to that of 1.38 for CPH model (age > 50 vs. ≤ 40). Also the covariates radiotherapy and chemotherapy were found to be significant (θ = 5.944, p-value: <0.001 and θ = 16, p-value: <0.001 respectively). Even though there were only minor differences in hazard rates, the concordance was higher for frailty than CPH model for all the covariates. Further the simulation study showed that the bias and root mean square error (RMSE) obtained for both the methods was almost the same and the concordance measures were higher for frailty model by 12–15%. Conclusions: We conclude that the frailty model is better compared to CPH model as it can account for unobserved random heterogeneity, and if the frailty coefficient doesn’t have an effect it gives exactly the same risk as that of CPH model and this has been established using survival concordance.
{"title":"Gamma frailty model for survival risk estimation: an application to cancer data","authors":"K. M. J. Krishna, T. Traison, Sejil Mariya Sebastian, P. George, A. Mathew","doi":"10.1515/em-2021-0005","DOIUrl":"https://doi.org/10.1515/em-2021-0005","url":null,"abstract":"Abstract Objectives: In time to event analysis, the risk for an event is usually estimated using Cox proportional hazards (CPH) model. But CPH model has the limitation of biased estimate due to unobserved hidden heterogeneity among the covariates, which can be tackled using frailty models. The best models were usually being identified using Akaike information criteria (AIC). Apart from AIC, the present study aimed to assess predictability of risk models using survival concordance measure. Methods: CPH model and frailty models were used to estimate the risk for breast cancer patient survival, and the frailty variable was assumed to follow gamma distribution. Schoenfeld global test was used to check the proportionality assumption. Survival concordance, AIC and simulation studies were used to identify the significance of frailty. Results: From the univariate analysis it was observed that for the covariate age, the frailty has a significant role (θ = 2.758, p-value: 0.0004) and the corresponding hazard rate was 1.93 compared to that of 1.38 for CPH model (age > 50 vs. ≤ 40). Also the covariates radiotherapy and chemotherapy were found to be significant (θ = 5.944, p-value: <0.001 and θ = 16, p-value: <0.001 respectively). Even though there were only minor differences in hazard rates, the concordance was higher for frailty than CPH model for all the covariates. Further the simulation study showed that the bias and root mean square error (RMSE) obtained for both the methods was almost the same and the concordance measures were higher for frailty model by 12–15%. Conclusions: We conclude that the frailty model is better compared to CPH model as it can account for unobserved random heterogeneity, and if the frailty coefficient doesn’t have an effect it gives exactly the same risk as that of CPH model and this has been established using survival concordance.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"109 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79257177","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: Vector-borne diseases speedily infest the human population. The control techniques must be applied to such ailment and work swiftly. We proposed a compartmental model of dengue disease by incorporating the standard incidence relation between susceptible vectors and infected humans to see the effect of manageable parameters of the model on the basic reproduction number. Methods: We compute the basic reproduction number by using the next -generation matrix method to study the local and global stability of disease free and endemic equilibrium points along with sensitivity analysis of the model. Results: Numerical results are explored the global behaviourism of disease-free/endemic state for a choice of arbitrary initial conditions. Also, the biting rate of vector population has more influence on the basic reproduction number as compared the other parameters. Conclusion: In this paper, shows that controlling the route of transmission of this disease is very important if we plan to restrict the transmission potential.
{"title":"Analysis for transmission of dengue disease with different class of human population","authors":"A. Dwivedi, Ram Keval","doi":"10.1515/em-2020-0046","DOIUrl":"https://doi.org/10.1515/em-2020-0046","url":null,"abstract":"Abstract Objectives: Vector-borne diseases speedily infest the human population. The control techniques must be applied to such ailment and work swiftly. We proposed a compartmental model of dengue disease by incorporating the standard incidence relation between susceptible vectors and infected humans to see the effect of manageable parameters of the model on the basic reproduction number. Methods: We compute the basic reproduction number by using the next -generation matrix method to study the local and global stability of disease free and endemic equilibrium points along with sensitivity analysis of the model. Results: Numerical results are explored the global behaviourism of disease-free/endemic state for a choice of arbitrary initial conditions. Also, the biting rate of vector population has more influence on the basic reproduction number as compared the other parameters. Conclusion: In this paper, shows that controlling the route of transmission of this disease is very important if we plan to restrict the transmission potential.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83013643","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 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":"397 1","pages":""},"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":"13 1","pages":""},"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":"125 1","pages":""},"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":"1998 1","pages":""},"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":"24 1","pages":""},"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":"46 1","pages":""},"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}
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