Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS

Liang, Kaihao

doi:10.1016/j.meegid.2020.104306

Cited by 140 publications

(131 citation statements)

References 16 publications

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“…Another important aspect of the first curve is its turning point, because it fixes also the change of the trend of the other two curves. 4. Surprisingly, after the turning point, the direction taken by the three curves is very different: the first decreases, the second increases and the third stays almost constant and begins a slow degrowth.…”

Section: Resultsmentioning

confidence: 93%

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Three pictures of COVID-19 behavior in Italy: similar growth and different degrowth

Feoli

Iannella

Benedetto

2020

Preprint

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In this short note, we present an analysis of the data regarding Coronavirus in Italy. We have used the official database provided by the Italian Civil Protection. Since the total number of people infected by the virus is uncertain, we have considered the trend of ICU patients, ICU patients plus deaths, and finally the sum of hospitalized patients plus the deceased. The growth of the corresponding curves is similar for all the three graphs while the trend after the turning point is completely different. We find that the curve of ICU patients can be a useful tool to monitor the behavior of epidemic and a model to predict the future evolution of COVID-19.

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Section: Resultsmentioning

confidence: 93%

“…Mathematical analysis of experimental data concerning COVID-19 has recently become of great interest [2][3][4][5]. The numbers regarding people who tested positive for the virus are certainly unrealistic.…”

Section: Introductionmentioning

confidence: 99%

Three pictures of COVID-19 behavior in Italy: similar growth and different degrowth

Feoli

Iannella

Benedetto

2020

Preprint

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“…Liang [17] uses differential equations and considers growth as proportional to the count of infected persons. This formulation leads to the solution involving exponential terms.…”

Section: Fanelli and Piazzamentioning

confidence: 99%

Multilevel Integrated Model with a Novel Systems Approach (MIMANSA) for Simulating the Spread of COVID-19

Welling¹,

Patel²,

Kulkarni

et al. 2020

Preprint

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COVID-19 has affected people's lives all over the world. It has created a perplexing situation about what actions one should and should not take. Mathematical modeling of biological systems is challenging and gives a different perspective, especially in decision making under multiple complex scenarios.MIMANSA for Simulating the Spread of COVID-19 Virus Page 2/35 number of hours spent with a silent carrier. The second provides control over the infection rate or the probability of a healthy person getting infected in the presence of a silent carrier. The third one allows the user to control lockdown effectiveness percentage, and the fourth one gives control over quarantine percentage.Inside the model, MIMANSA differentiates between virus-infected patients, silent carriers, and healthy carriers. MIMANSA has the capability to consider variations in virus activity levels of every asymptomatic patient, varying the exposure to the virus, and varying the infection rate depending on the person's immunity.MIMANSA can simulate scenarios to study the impact of many different conditions simultaneously. MIMANSA assists public health officials in complex decision making, enables scientists in projecting the SARS-CoV-2 virus spread, and aids hospital administrators in management. MIMANSA will play a significant role in finding the balance between the effect of strict lockdown on the economy vs. the marginally high number of COVID-19 patients with a bit relaxed lockdown.

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“…The COVID-19 cases were soon exported to other Chinese cities and overseas (6), and the travel-related risk of disease spreading was suggested by previous studies (4,(7)(8)(9). The risks of rapid spreading were evaluated based on the early surveillance data and also compared to other previous respiratory infectious diseases (5,(10)(11)(12)(13)(14). Since the rst con rmed imported case in Hong Kong on January 23 (15), the local government has implemented a series of control and prevention measures for COVID-19, including enhanced border screening and tra c restrictions (16,17).…”

Section: Introductionmentioning

confidence: 99%

Estimating the serial interval of the novel coronavirus disease (COVID-19): A statistical analysis using the public data in Hong Kong from January 16 to February 15, 2020

Zhao

Gao

Zhuang

et al. 2020

Preprint

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Background: The emerging virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a large outbreak of novel coronavirus disease (COVID-19) in Wuhan, China since December 2019. As of February 15, there were 56 COVID-19 cases confirmed in Hong Kong since the first case with symptom onset on January 23, 2020. Methods: Based on the publicly available surveillance data, we identified 21 transmission events, which occurred in Hong Kong, and had primary cases known, as of February 15, 2020. An interval censored likelihood framework is adopted to fit three different distributions, Gamma, Weibull and lognormal, that govern the SI of COVID-19. We selection the distribution according to the Akaike information criterion corrected for small sample size (AICc). Findings: We found the Lognormal distribution performed lightly better than the other two distributions in terms of the AICc. Assuming a Lognormal distribution model, we estimated the mean of SI at 4.9 days (95%CI: 3.6−6.2) and SD of SI at 4.4 days (95%CI: 2.9−8.3) by using the information of all 21 transmission events in Hong Kong. Conclusion: The SI of COVID-19 may be shorter than the preliminary estimates in previous works. Given the likelihood that SI could be shorter than the incubation period, pre-symptomatic transmission may occur, and extra efforts on timely contact tracing and quarantine are crucially needed in combating the COVID-19 outbreak.

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Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS

Cited by 140 publications

References 16 publications

Three pictures of COVID-19 behavior in Italy: similar growth and different degrowth

Three pictures of COVID-19 behavior in Italy: similar growth and different degrowth

Multilevel Integrated Model with a Novel Systems Approach (MIMANSA) for Simulating the Spread of COVID-19

Estimating the serial interval of the novel coronavirus disease (COVID-19): A statistical analysis using the public data in Hong Kong from January 16 to February 15, 2020

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