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

Jia, Wenyu; Han, Kihye; Song, Yang; Cao, Wenzhe; Wang, Shengshu; Yang, Shanshan; Wang, Jianwei; Kou, Fuyin; Tai, Penggang; Li, Jing; Liu, Miao; He, Yao

doi:10.1101/2020.03.18.20038570

Cited by 53 publications

(75 citation statements)

References 29 publications

(40 reference statements)

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“…There have been many modelling approaches presented by various mathematicians and researchers in recent time for different countries. Models for China [3][4][5][6][7], Italy [8][9][10][11], France [8] [12], USA [13][14][15], and South Korea [16][17]. For Indian region, the studies are very less till now and some notable work has been done by people from ICMR [18] and other researchers [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Trend Analysis and Forecasting of COVID-19 outbreak in India

Gupta

2020

Preprint

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COVID-19 is spreading really fast around the world. The current study describes the situation of the outbreak of this disease in India and predicts the number of cases expected to rise in India. The study also discusses the regional analysis of Indian states and presents the preparedness level of India in combating this outbreak. The study uses exploratory data analysis to report the current situation and uses time-series forecasting methods to predict the future trends. The data has been considered from the repository of John Hopkins University and covers up the time period from 30th January 2020 when the first case occurred in India till the end of 24th March 2020 when the Prime Minister of India declared a complete lockdown in the country for 21 days starting 25th March 2020. The major findings show that number of infected cases in India is rising quickly with the average infected cases per day rising from 10 to 73 from the first case to the 300th case. The current mortality rate for India stands around 1.9. Kerala and Maharashtra are the top two infected states in India with more than 100 infected cases reported in each state, respectively. A total of 25 states have reported at least one infected case, however only 8 of them have reported deaths due to COVID-19. The ARIMA model prediction shows that the infected cases in India may reach up to 700 thousands in next 30 days in worst case scenario while most optimistic scenario may restrict the numbers up to 1000-1200. Also, the average forecast by ARIMA model in next 30 days is around 7000 patients from the current numbers of 536. Based on the forecasting model by Holts linear trends, an expected 3 million people may get infected if control measures are not taken in the near future. This study will be useful for the key stakeholders like Government Officials and Medical Practitioners in assessing the trends for India and preparing a combat plan with stringent measures. Also, this study will be helpful for data scientists, statisticians, mathematicians and analytics professionals in predicting outbreak numbers with better accuracy.

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Section: Introductionmentioning

confidence: 99%

Trend Analysis and Forecasting of COVID-19 outbreak in India

Gupta

2020

Preprint

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“…The current situation of China, mainly six provinces having most of the cases has been analysed in [12] [13] [14] whereas the same for Germany [15], Portugal [16], United States [17], Italy [18], Brazil [19] has been discussed in the recent literature. Although the spread of COVID-19 has been exponentially increased all over the world, still the fatality rate is not so alarming which gives residents of every country to re-establish the confidence to overcome and fight this pandemic together.…”

Section: Literature Surveymentioning

confidence: 99%

“…Finite impulse response filter can be used to forecast the amount of affected and recovered population by previously discussed time dependent SIR model. After calculating the transmission rate and recovery rate according to (14) and (15), the ridge regression is used to solve the optimization problems of (18) and (19) keeping the constraints of (16), (17) to learn the pattern of coefficients of finite impulse response filter.…”

Section: Detection Of Number Of Affected Persons and Recovered Personsmentioning

confidence: 99%

“…For predicting the affected and recovered population, r(t) and a(t) have been replaced by r (t) and a (t) in equation 17and (18) where the predicted number of affected people and recovered people at time t = T are denoted as I (T ) and R (T ). This leads to…”

Section: Detection Of Number Of Affected Persons and Recovered Personsmentioning

confidence: 99%

“…Step 2: Train them with ridge regression using (18) and (19) Step 3: Calculate r(T-1) and a'(T-1) using (16) and (17) Step 4: Calculate I (T ) and R (T ) for the next day T using (20) and 21Step 5: If T ≤t≤T+Fdo Calculate r (t) and a (t) using (16) and (17) Estimate I (t + 1) and R (t + 1) using (22) and (23) respectively End if Step 6: End…”

Section: Detection Of Number Of Affected Persons and Recovered Personsmentioning

confidence: 99%

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Mathematical Modelling For Decision Making of Lockdown during COVID-19

Ghosh

Roy

Biswas

2020

Preprint

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Due to the recent worldwide outbreak of COVID-19, there has been a huge change in our lifestyle and it has a severe impact in different fields like finance, education, business, travel and tourism, economy in all the affected countries. In this scenario, people have to be careful and cautious about the symptoms and should act accordingly. Accurate predictions of factors, like the end date of the pandemic, duration of lockdown and spreading trend can guide us through the situation and precautions should be taken wisely. Multiple attempts have been made to model the virus transmission, but none of them has investigated it at a global level and concepts like recovery trend analysis in the developed and developing countries have not been discussed ever. The novelty of our proposed work lies here. In this paper, we have analysed the nature of spreading of the said disease using Time Dependent Discrete Susceptible Infected Recovered (TDDSIR) model on the data collected from various platforms and then, fifteen countries from first, second and third world have been considered to have an idea of probable future projections of pandemic. Experimental findings proved that proper social distancing measures during lockdown has been a controller of the disease transmission trend as the basic reproduction number, being actually the transmission rate and not the number of infectives, decreases with the strict lockdown decisions made by different countries. However, people should be more aware of the consequences for quick recovery from the various obstacles of the current situation.

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Estimation of novel coronavirus (COVID‐19) reproduction number and case fatality rate: A systematic review and meta‐analysis

Ahammed

Anjum

Rahman

et al. 2021

Health Science Reports

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Background and aims Realizing the transmission potential and the magnitude of the coronavirus disease 2019 (COVID‐19) aids public health monitoring, strategies, and preparation. Two fundamental parameters, the basic reproduction number ( R 0 ) and case fatality rate (CFR) of COVID‐19, help in this understanding process. The objective of this study was to estimate the R 0 and CFR of COVID‐19 and assess whether the parameters vary in different regions of the world. Methods We carried out a systematic review to find the reported estimates of the R 0 and the CFR in articles from international databases between January 1 and August 31, 2020. Random‐effect models and Forest plots were implemented to evaluate the mean effect size of R 0 and the CFR. Furthermore, R 0 and CFR of the studies were quantified based on geographic location, the tests/thousand population, and the median population age of the countries where the studies were conducted. To assess statistical heterogeneity among the selected articles, the I 2 statistic and the Cochran's Q test were used. Results Forty‐five studies involving R 0 and 34 studies involving CFR were included. The pooled estimation of R 0 was 2.69 (95% CI: 2.40, 2.98), and that of the CFR was 2.67 (2.25, 3.13). The CFR in different regions of the world varied significantly, from 2.49 (2.08, 2.94) in Asia to 3.40 (2.81, 4.04) in North America. We observed higher mean CFR values for the countries with lower tests (3.15 vs 2.16) and greater median population age (3.13 vs 2.27). However, R 0 did not vary significantly in different regions of the world. Conclusions An R 0 of 2.69 and a CFR of 2.67 indicate the severity of the COVID‐19. Although R 0 and CFR may vary over time, space, and demographics, we recommend considering these figures in control and prevention measures.

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

Abstract: 14

Cited by 53 publications

References 29 publications

Trend Analysis and Forecasting of COVID-19 outbreak in India

Trend Analysis and Forecasting of COVID-19 outbreak in India

Mathematical Modelling For Decision Making of Lockdown during COVID-19

Estimation of novel coronavirus (COVID‐19) reproduction number and case fatality rate: A systematic review and meta‐analysis

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