Transmission dynamics of the COVID-19 epidemic in India and modeling optimal lockdown exit strategies

Gupta, Mohak; Mohanta, Rishika; Rao, Angoori Gnaneshwar; Parameswaran, Giridara Gopal; Agarwal, Mudit; Arora, Madhu; Mazumder, Archisman; Lohiya, Ayush; Behera, Priyamadhaba; Bansal, Agam; Kumar, Rohit; Meena, Ved Prakash; Tiwari, Pawan; Mohan, Anant; Bhatnagar, Sushma

doi:10.1016/j.ijid.2020.11.206

Cited by 52 publications

(43 citation statements)

References 45 publications

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“…This study finds preliminary R 0 of Bangladesh was 2.02 (1.33–3.28,95% CI). Our results are similar to previously reported R 0 in China [ 17 , 27 ] and neighboring country India [ 28 ]. After a week of nation-wide lockdown, R t decreased substantially from early April 2020 in Bangladesh, which corroborates with those of [ 29 , 30 ].…”

Section: Discussionsupporting

confidence: 93%

Spatio-temporal estimation of basic and effective reproduction number of COVID-19 and post-lockdown transmissibility in Bangladesh

et al. 2021

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The ongoing COVID-19 pandemic has caused unprecedented public health concern in Bangladesh. This study investigated the role of Non-Pharmaceutical Interventions on COVID-19 transmission and post-lockdown scenarios of 64 administrative districts and the country as a whole based on the spatiotemporal variations of effective reproduction number ( R t ) of COVID-19 incidences. The daily confirmed COVID-19 data of Bangladesh and its administrative districts from March 8, 2020, to March 10, 2021, were used to estimate R t . This study finds that the maximum value of R t reached 4.15 (3.43, 4.97, 95% CI) in late March 2020, which remained above 1 afterwards in most of the districts. Containment measures are moderately effective in reducing transmission by 24.03%. The R t was established below 1 from early December 2020 for overall Bangladesh and a gradual increase of R t above 1 has been seen from early February 2021. The basic reproduction number ( R 0 ) in Bangladesh probably varied around 2.02 (1.33–3.28, 95% CI). This study finds a significant positive correlation (r = 0.75) between population density and COVID-19 incidence and explaining 56% variation in Bangladesh. The findings of this study are expected to support the policymakers to adopt appropriate measures for curbing the COVID-19 transmission effectively.

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Section: Discussionsupporting

confidence: 93%

Spatio-temporal estimation of basic and effective reproduction number of COVID-19 and post-lockdown transmissibility in Bangladesh

et al. 2021

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“…At the same time, there have been multiple adaptation of health care systems to the current pandemic, which require time to implement and roll out, and the strict control measures implemented in Sabah, and adoption of low-risk quarantine centers, has allowed significant slack to be cut on tertiary hospitals so that they can rightfully focus on unwell Covid-19 patients. The model also demonstrates that if MCOs are relaxed overly prematurely, what happens is that reproductive numbers and growth factors can climb prematurely ( Ferguson et al., 2020 ; Gupta et al., 2021 ; Leung & Wu, 2020 ).…”

Section: Discussionmentioning

confidence: 98%

Monitoring the impact of Movement Control Order (MCO) in flattening the cummulative daily cases curve of Covid-19 in Malaysia: A generalized logistic growth modeling approach

Pang

Kamu

Kassim

et al. 2021

Infectious Disease Modelling

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Introduction COVID-19 has affected almost every country in the world, which causing many negative implications in terms of education, economy and mental health. Worryingly, the trend of second or third wave of the pandemic has been noted in multiple regions despite early success of flattening the curve, such as in the case of Malaysia, post Sabah state election in September 2020. Hence, it is imperative to predict ongoing trend of COVID-19 to assist crucial policymaking in curbing the transmission. Method Generalized logistic growth modelling (GLM) approach was adopted to make prediction of growth of cases according to each state in Malaysia. The data was obtained from official Ministry of Health Malaysia daily report, starting from 26 September 2020 until 1 January 2021. Result Sabah, Johor, Selangor and Kuala Lumpur are predicted to exceed 10,000 cumulative cases by 2 February 2021. Nationally, the growth factor has been shown to range between 0.25 to a peak of 3.1 throughout the current Movement Control Order (MCO). The growth factor range for Sabah ranged from 1.00 to 1.25, while Selangor, the state which has the highest case, has a mean growth factor ranging from 1.22 to 1.52. The highest growth rates reported were in WP Labuan for the time periods of 22 Nov - 5 Dec 2020 with growth rates of 4.77. States with higher population densities were predicted to have higher cases of COVID-19. Conclusion GLM is helpful to provide governments and policymakers with accurate and helpful forecasts on magnitude of epidemic and peak time. This forecast could assist government in devising short- and long-term plan to tackle the ongoing pandemic.

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“…Hong Kong had successfully limited its number of confirmed cases during the early stages of the pandemic 36 , but has since then seen several resurgences 37 . Although India responded rapidly by imposing a strict lockdown, it is now one of the worst-affected countries in Asia, and has the second highest number of infections worldwide 38 . The third dataset contains the offspring distribution for 795 cases in Rwanda (personal communication), which had a cumulative number of COVID-19 cases of 6.4 per 10,000 population on December 31, 2020.…”

Section: Methodsmentioning

confidence: 99%

Quantifying superspreading for COVID-19 using Poisson mixture distributions

Kremer

Torneri

Boesmans

et al. 2021

Sci Rep

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The number of secondary cases, i.e. the number of new infections generated by an infectious individual, is an important parameter for the control of infectious diseases. When individual variation in disease transmission is present, like for COVID-19, the distribution of the number of secondary cases is skewed and often modeled using a negative binomial distribution. However, this may not always be the best distribution to describe the underlying transmission process. We propose the use of three other offspring distributions to quantify heterogeneity in transmission, and we assess the possible bias in estimates of the mean and variance of this distribution when the data generating distribution is different from the one used for inference. We also analyze COVID-19 data from Hong Kong, India, and Rwanda, and quantify the proportion of cases responsible for 80% of transmission, $$p_{80\%}$$ p 80 % , while acknowledging the variation arising from the assumed offspring distribution. In a simulation study, we find that variance estimates may be biased when there is a substantial amount of heterogeneity, and that selection of the most accurate distribution from a set of distributions is important. In addition we find that the number of secondary cases for two of the three COVID-19 datasets is better described by a Poisson-lognormal distribution.

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Transmission dynamics of the COVID-19 epidemic in India and modeling optimal lockdown exit strategies

Cited by 52 publications

References 45 publications

Spatio-temporal estimation of basic and effective reproduction number of COVID-19 and post-lockdown transmissibility in Bangladesh

Spatio-temporal estimation of basic and effective reproduction number of COVID-19 and post-lockdown transmissibility in Bangladesh

Monitoring the impact of Movement Control Order (MCO) in flattening the cummulative daily cases curve of Covid-19 in Malaysia: A generalized logistic growth modeling approach

Quantifying superspreading for COVID-19 using Poisson mixture distributions

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