Extended SIR prediction of the epidemics trend of COVID-19 in Italy and compared with Hunan, China

AbstractBackgroundCoronavirus Disease 2019 (COVID-19) is currently a global public health threat. Outside of China, Italy is one of the most suffering countries with the COVID-19 epidemic. It is important to predict the epidemics trend of COVID-19 epidemic in Italy to help develop public health strategies.MethodsWe used time-series data of COVID-19 from Jan 22,2020 to Mar 16,2020. An infectious disease dynamic extended susceptible-infected-removed (eSIR) model, which covers the effects of different intervention measures in dissimilar periods, was applied to estimate the epidemic trend in Italy. The basic reproductive number was estimated using Markov Chain Monte Carlo methods and presented using the resulting posterior mean and 95% credible interval (CI). Hunan, with similar total number of populations in Italy, was used as a comparative item.ResultsIn the eSIR model, we estimated that the basic reproductive number for COVID-19 was respectively 4.10 (95% CI: 2.15–6.77) in Italy and 3.15(95% CI: 1.71–5.21) in Hunan. There would be totally 30 086 infected cases (95%CI:7920-81 869) under the current country blockade and the endpoint would be Apr 25 (95%CI: Mar 30 to Aug 07) in Italy. If the country blockade is imposed 5 day later, the total number of infected cases would expand the infection scale 1.50 times.ConclusionItaly’s current strict measures can efficaciously prevent the further spread of COVID-19 and should be maintained. Necessary strict public health measures be implemented as soon as possible in other European countries with a high number of COVID-19 cases. The most effective strategy needs to be confirmed in further studies.

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Forecasting COVID-19 pandemic in Poland according to government regulations and people behavior

10.1101/2020.05.26.20112458 ◽

2020 ◽

Cited By ~ 1

Author(s):

Magdalena Orzechowska ◽

Andrzej K. Bednarek

Keyword(s):

Time Series Data ◽

Basic Reproductive Number ◽

Series Data ◽

Reproductive Number ◽

Protective Measures ◽

Early Introduction ◽

Care Capacity ◽

Public Health Strategies ◽

The Impact ◽

Epidemic Forecasting

AbstractThe coronavirus disease 2019 (COVID-19) outbreak is a worldwide pandemic problem that started in China in December 2019 and within a few months spread to all continents. Very high infectivity of SARS-CoV-2 virus and substantial disease severity caused medical care capacity shortage in many countries. Therefore, real-time epidemic forecasting of the COVID-19 is useful to plan public health strategies like country lockdown and healthcare reorganization.We used extended susceptible-infected-removed (eSIR) model to predict the epidemic trend of COVID-19 in Poland under different scenarios of the lockdown and lockdown removal. We used time-series data of SARS-CoV-2 infection from March 4 to May 22 2020. Our forecast includes the impact of a timeline of preventive measures introduced in Poland. Using eSIR algorithm we estimated the basic reproductive number and a total number of infections under different epidemic trend scenarios.Using eSIR modeling we estimated that the basic reproductive number in Poland concerning different scenarios of the lockdown removal is in a range of 3.91-4.79. The lowest predicted number of infected cases would be 263 900 (0 - 1 734 200, 95%CI) if the strict protective measures were maintained until the end of September. However, under different scenarios of precautions removal, a total number of infected cases may exceed one million within the next year.Relatively early introduction of strong precautions in Poland significantly slowed down epidemic spread in Poland in comparison with other European countries like Italy or Spain. However, early removal of protective measures may result in a significant increase in infection. Data shows that the number of new COVID-19 cases in Poland beyond May 18 is linear what could be a prognosis of a duration of the epidemic exceeding 300 days.

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An Epidemiological Model Considering Isolation to Predict COVID-19 Trends in Tokyo, Japan: Numerical Analysis (Preprint)

10.2196/preprints.23624 ◽

2020 ◽

Author(s):

Motoaki Utamura ◽

Makoto Koizumi ◽

Seiichi Kirikami

Keyword(s):

Public Health ◽

Transmission Rate ◽

Time Lag ◽

Sir Model ◽

Epidemiological Model ◽

Basic Reproductive Number ◽

Polymerase Chain Reaction Test ◽

Series Data ◽

Reproductive Number ◽

Alternative Formulation

BACKGROUND COVID-19 currently poses a global public health threat. Although Tokyo, Japan, is no exception to this, it was initially affected by only a small-level epidemic. Nevertheless, medical collapse nearly happened since no predictive methods were available to assess infection counts. A standard susceptible-infectious-removed (SIR) epidemiological model has been widely used, but its applicability is limited often to the early phase of an epidemic in the case of a large collective population. A full numerical simulation of the entire period from beginning until end would be helpful for understanding COVID-19 trends in (separate) counts of inpatient and infectious cases and can also aid the preparation of hospital beds and development of quarantine strategies. OBJECTIVE This study aimed to develop an epidemiological model that considers the isolation period to simulate a comprehensive trend of the initial epidemic in Tokyo that yields separate counts of inpatient and infectious cases. It was also intended to induce important corollaries of governing equations (ie, effective reproductive number) and equations for the final count. METHODS Time-series data related to SARS-CoV-2 from February 28 to May 23, 2020, from Tokyo and antibody testing conducted by the Japanese government were adopted for this study. A novel epidemiological model based on a discrete delay differential equation (apparent time-lag model [ATLM]) was introduced. The model can predict trends in inpatient and infectious cases in the field. Various data such as daily new confirmed cases, cumulative infections, inpatients, and PCR (polymerase chain reaction) test positivity ratios were used to verify the model. This approach also derived an alternative formulation equivalent to the standard SIR model. RESULTS In a typical parameter setting, the present ATLM provided 20% less infectious cases in the field compared to the standard SIR model prediction owing to isolation. The basic reproductive number was inferred as 2.30 under the condition that the time lag <i>T</i> from infection to detection and isolation is 14 days. Based on this, an adequate vaccine ratio to avoid an outbreak was evaluated for 57% of the population. We assessed the date (May 23) that the government declared a rescission of the state of emergency. Taking into consideration the number of infectious cases in the field, a date of 1 week later (May 30) would have been most effective. Furthermore, simulation results with a shorter time lag of <i>T</i>=7 and a larger transmission rate of α=1.43α0 suggest that infections at large should reduce by half and inpatient numbers should be similar to those of the first wave of COVID-19. CONCLUSIONS A novel mathematical model was proposed and examined using SARS-CoV-2 data for Tokyo. The simulation agreed with data from the beginning of the pandemic. Shortening the period from infection to hospitalization is effective against outbreaks without rigorous public health interventions and control.

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Analysis of the attention to COVID-19 epidemic based on visibility graph network

Modern Physics Letters B ◽

10.1142/s0217984921503164 ◽

2021 ◽

pp. 2150316

Author(s):

Qingxiang Feng ◽

Haipeng Wei ◽

Jun Hu ◽

Wenzhe Xu ◽

Fan Li ◽

...

Keyword(s):

Public Health ◽

Time Series ◽

Time Series Data ◽

Search Behavior ◽

Series Data ◽

Global Public Health ◽

Health Crisis ◽

Time Space ◽

Epidemic Period ◽

Visibility Algorithm

Most of the existing researches on public health events focus on the number and duration of events in a year or month, which are carried out by regression equation. COVID-19 epidemic, which was discovered in Wuhan, Hubei Province, quickly spread to the whole country, and then appeared as a global public health event. During the epidemic period, Chinese netizens inquired about the dynamics of COVID-19 epidemic through Baidu search platform, and learned about relevant epidemic prevention information. These groups’ search behavior data not only reflect people’s attention to COVID-19 epidemic, but also contain the stage characteristics and evolution trend of COVID-19 epidemic. Therefore, the time, space and attribute laws of propagation of COVID-19 epidemic can be discovered by deeply mining more information in the time series data of search behavior. In this study, it is found that transforming time series data into visibility network through the principle of visibility algorithm can dig more hidden information in time series data, which may help us fully understand the attention to COVID-19 epidemic in Chinese provinces and cities, and evaluate the deficiencies of early warning and prevention of major epidemics. What’s more, it will improve the ability to cope with public health crisis and social decision-making level.

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The basic reproductive number of Ebola and the effects of public health measures: the cases of Congo and Uganda

Journal of Theoretical Biology ◽

10.1016/j.jtbi.2004.03.006 ◽

2004 ◽

Vol 229 (1) ◽

pp. 119-126 ◽

Cited By ~ 290

Author(s):

G. Chowell ◽

N.W. Hengartner ◽

C. Castillo-Chavez ◽

P.W. Fenimore ◽

J.M. Hyman

Keyword(s):

Public Health ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Health Measures

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An Epidemiological Model Considering Isolation to Predict COVID-19 Trends in Tokyo, Japan: Numerical Analysis

JMIR Public Health and Surveillance ◽

10.2196/23624 ◽

2020 ◽

Vol 6 (4) ◽

pp. e23624

Author(s):

Motoaki Utamura ◽

Makoto Koizumi ◽

Seiichi Kirikami

Keyword(s):

Public Health ◽

Transmission Rate ◽

Time Lag ◽

Sir Model ◽

Epidemiological Model ◽

Basic Reproductive Number ◽

Polymerase Chain Reaction Test ◽

Series Data ◽

Reproductive Number ◽

Alternative Formulation

Background COVID-19 currently poses a global public health threat. Although Tokyo, Japan, is no exception to this, it was initially affected by only a small-level epidemic. Nevertheless, medical collapse nearly happened since no predictive methods were available to assess infection counts. A standard susceptible-infectious-removed (SIR) epidemiological model has been widely used, but its applicability is limited often to the early phase of an epidemic in the case of a large collective population. A full numerical simulation of the entire period from beginning until end would be helpful for understanding COVID-19 trends in (separate) counts of inpatient and infectious cases and can also aid the preparation of hospital beds and development of quarantine strategies. Objective This study aimed to develop an epidemiological model that considers the isolation period to simulate a comprehensive trend of the initial epidemic in Tokyo that yields separate counts of inpatient and infectious cases. It was also intended to induce important corollaries of governing equations (ie, effective reproductive number) and equations for the final count. Methods Time-series data related to SARS-CoV-2 from February 28 to May 23, 2020, from Tokyo and antibody testing conducted by the Japanese government were adopted for this study. A novel epidemiological model based on a discrete delay differential equation (apparent time-lag model [ATLM]) was introduced. The model can predict trends in inpatient and infectious cases in the field. Various data such as daily new confirmed cases, cumulative infections, inpatients, and PCR (polymerase chain reaction) test positivity ratios were used to verify the model. This approach also derived an alternative formulation equivalent to the standard SIR model. Results In a typical parameter setting, the present ATLM provided 20% less infectious cases in the field compared to the standard SIR model prediction owing to isolation. The basic reproductive number was inferred as 2.30 under the condition that the time lag T from infection to detection and isolation is 14 days. Based on this, an adequate vaccine ratio to avoid an outbreak was evaluated for 57% of the population. We assessed the date (May 23) that the government declared a rescission of the state of emergency. Taking into consideration the number of infectious cases in the field, a date of 1 week later (May 30) would have been most effective. Furthermore, simulation results with a shorter time lag of T=7 and a larger transmission rate of α=1.43α0 suggest that infections at large should reduce by half and inpatient numbers should be similar to those of the first wave of COVID-19. Conclusions A novel mathematical model was proposed and examined using SARS-CoV-2 data for Tokyo. The simulation agreed with data from the beginning of the pandemic. Shortening the period from infection to hospitalization is effective against outbreaks without rigorous public health interventions and control.

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Isolation Considered Epidemiological Model for the Prediction of COVID-19 Trend in Tokyo, Japan

10.1101/2020.07.31.20165829 ◽

2020 ◽

Cited By ~ 2

Author(s):

Motoaki Utamura ◽

Makoto Koizumi ◽

Seiichi Kirikami

Keyword(s):

Public Health ◽

Public Health Intervention ◽

Epidemiological Model ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Alternative Formulation ◽

Global Public Health ◽

Economic Activities ◽

Delay Differential ◽

Spread Of Infection

Background: Coronavirus Disease 2019 (COVID19) currently poses a global public health threat. Although no exception, Tokyo, Japan was affected at first by only a small epidemic. Medical collapse nevertheless nearly happened because no predictive method existed for counting patients. A standard SIR epidemiological model and its derivatives predict susceptible, infectious, and removed (recovered/deaths) cases but ignore isolation of confirmed cases. Predicting COVID19 trends with hospitalized and infectious people in field separately is important to prepare beds and develop quarantine strategies. Methods: Time-series COVID19 data from February 28 to May 23, 2020 in Tokyo were adopted for this study. A novel epidemiological model based on delay differential equation was proposed. The model can evaluate patients in hospitals and infectious cases in the field. Various data such as daily new cases, cumulative infections, patients in hospital, and PCR test positivity ratios were used to examine the model. This approach derived an alternative formulation equivalent to the standard SIR model. Its results were compared quantitatively with those of the present isolation model. Results: The basic reproductive number, inferred as 2.30, is a dimensionless parameter composed of modeling parameters. Effects of intervention to mitigate the epidemic spread were assessed a posteriori. An exit policy of how and when to release a statement of emergency was also assessed using the model. Furthermore, results suggest that the rapid isolation of infectious cases has a large potential to effectively mitigate the spread of infection and restores social and economic activities safely. Conclusions: A novel mathematical model was proposed and examined using COVID19 data for Tokyo. Results show that shortening the period from infection to hospitalization is effective against outbreak without rigorous public health intervention and control. Faster and precise case cluster detection and wider and quicker introduction of testing measures are strongly recommended.

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Epidemic Landscape and Forecasting of SARS-CoV-2 in India

10.1101/2020.04.14.20065151 ◽

2020 ◽

Cited By ~ 5

Author(s):

Aravind Lathika Rajendrakumar ◽

Anand Thakarakkattil Narayanan Nair ◽

Charvi Nangia ◽

Prabhal Kumar Chourasia ◽

Mehul Kumar Chourasia ◽

...

Keyword(s):

Growth Rate ◽

Doubling Time ◽

Time Series Data ◽

Epidemiologic Studies ◽

Basic Reproductive Number ◽

Series Data ◽

Reproductive Number ◽

Peak Time ◽

Local Cluster ◽

The Mean

BACKGROUNDIndia was one of the countries to institute strict measures for SARS-CoV-2 control in early phase. Since, then, the epidemic growth trajectory was slow before registering an explosion of cases due to local cluster transmissions.METHODSWe estimated growth rate and doubling time of SARS-CoV-2 for India and high burden states using crowd sourced time series data. Further, we also estimated Basic Reproductive Number (R0) and time dependent reproductive number (Rt) using serial intervals from the data. We compared the R0 estimated from five different methods and R0 from SB was further used in analysis. We modified standard SIR models to SIRD model to accommodate deaths using R0 with the Sequential Bayesian method (SBM) for simulation in SIRD models.RESULTSOn an average, 2.8 individuals were infected by an index case. The mean serial interval was 3.9 days. The R0 estimated from different methods ranged from 1.43 to 1.85. The mean time to recovery was 14 ± 5.3 days. Daily epidemic growth rate of India was 0.16 [95%CI; 0.14, 0.17] with a doubling time of 4.30 days [95%CI; 3.96, 4.70]. From the SIRD model, it can be deduced that the peak of SARS-CoV-2 in India will be around mid-July to early August 2020 with around 12.5% of population likely to be infected at the peak time.CONCLUSIONSThe pattern of spread of SARS-CoV-2 in India is suggestive of community transmission. There is a need to increase fund for infectious disease research and epidemiologic studies. All the current gains may be reversed rapidly if air travel and social mixing resumes rapidly. For the time being, these must be resumed only in a phased manner, and should be back to normal levels only after we are prepared to deal with the disease with efficient tools like vaccine or a medicine.

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Modelling the dynamics of a public health care system: evidence from time-series data

Applied Economics ◽

10.1080/00036846.2011.568407 ◽

2011 ◽

Vol 44 (23) ◽

pp. 2955-2968 ◽

Cited By ~ 1

Author(s):

Fabrizio Iacone ◽

Steve Martin ◽

Luigi Siciliani ◽

Peter C. Smith

Keyword(s):

Public Health ◽

Health Care ◽

Time Series ◽

Health Care System ◽

Time Series Data ◽

Public Health Care ◽

Series Data ◽

Public Health Care System ◽

Care System

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Spatiotemporal patterns and progression of the Delta variant of COVID-19 and their health intervention linkages in Southeast Asia

10.1101/2021.12.20.21268140 ◽

2021 ◽

Author(s):

Wei Luo ◽

Zhaoyin Liu ◽

Yuxuan Zhou ◽

Yumin Zhao ◽

Yunyue Elita Li ◽

...

Keyword(s):

Public Health ◽

Southeast Asia ◽

Spatiotemporal Analysis ◽

Health Interventions ◽

Disease Spread ◽

Epidemic Dynamics ◽

Health Measures ◽

Global Pandemic ◽

Public Health Strategies ◽

Time Scan

The global pandemic of COVID-19 presented an unprecedented challenge to all countries in the world, among which Southeast Asia (SEA) countries managed to maintain and mitigate the first wave of COVID-19 in 2020. However, these countries were caught in the crisis after the Delta variant was introduced to SEA, though many countries had immediately implemented non-pharmaceutical intervention (NPI) measures along with vaccination in order to contain the disease spread. To investigate the potential linkages between epidemic dynamics and public health interventions, we adopted a prospective space-time scan method to conduct spatiotemporal analysis at the district level in the seven selected countries in SEA from June 2021 to October 2021. Results reveal the spatial and temporal propagation and progression of COVID-19 risks relative to public health measures implemented by different countries. Our research benefits continuous improvements of public health strategies in preventing and containing this pandemic.

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Effect of a Publicly Accessible Disclosure System on Food Safety Inspection Scores in Retail and Food Service Establishments

Journal of Food Protection ◽

10.4315/0362-028x.jfp-16-293 ◽

2017 ◽

Vol 80 (7) ◽

pp. 1188-1192 ◽

Cited By ~ 2

Author(s):

Jihee Choi ◽

Robert L. Scharff

Keyword(s):

Public Health ◽

Food Safety ◽

Time Series Data ◽

Food Service ◽

Ordinary Least Squares ◽

Series Data ◽

Local Health ◽

Public Health Department ◽

Foodborne Disease ◽

Least Squares Regression

ABSTRACT The increased frequency with which people are dining out coupled with an increase in the publicity of foodborne disease outbreaks has led the public to an increased awareness of food safety issues associated with food service establishments. To accommodate consumer needs, local health departments have increasingly publicized food establishments' health inspection scores. The objective of this study was to estimate the effect of the color-coded inspection score disclosure system in place since 2006 in Columbus, OH, by controlling for several confounding factors. This study incorporated cross-sectional time series data from food safety inspections performed from the Columbus Public Health Department. An ordinary least squares regression was used to assess the effect of the new inspection regime. The introduction of the new color-coded food safety inspection disclosure system increased inspection scores for all types of establishments and for most types of inspections, although significant differences were found in the degree of improvement. Overall, scores increased significantly by 1.14 points (of 100 possible). An exception to the positive results was found for inspections in response to foodborne disease complaints. Scores for these inspections declined significantly by 10.2 points. These results should be useful for both food safety researchers and public health decision makers.

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