An Evaluation of COVID-19 in Italy: A data-driven modeling analysis

Ding, Yongmei; Gao, Liyuan

doi:10.21203/rs.3.rs-28146/v1

Cited by 7 publications

(9 citation statements)

References 6 publications

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“…But if the TD is lower than 11.5°C, it presents a reverse linear relationship that the greater the temperature difference, the fewer updates of COVID-19. External temperature changes that affect the transmission mechanism of COVID-19 mainly reflect in two aspects: one is the impact of temperature on the survival of COVID-19 virus [25][26][27], which has been confirmed in early studies on SARS virus [28][29][30][31]; second, external temperature changes cause the migration and mobility of population [32][33][34]. Our study sheds some light on the non-linear relationship between ambient temperature and new confirmed cases of COVID-19, which verifies that the number of new confirmed cases of COVID-19 may increase daily without any public health interventions if the weather changes dramatically.…”

Section: Discussionmentioning

confidence: 94%

Non-linear link between temperature difference and COVID-19: Excluding the effect of population density

Ding

Gao

Shao³

2021

J Infect Dev Ctries

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Introduction: The spatiotemporal patterns of Corona Virus Disease 2019 (COVID-19) is detected in the United States, which shows temperature difference (TD) with cumulative hysteresis effect significantly changes the daily new confirmed cases after eliminating the interference of population density. Methodology: The nonlinear feature of updated cases is captured through Generalized Additive Mixed Model (GAMM) with threshold points; Exposure-response curve suggests that daily confirmed cases is changed at the different stages of TD according to the threshold points of piecewise function, which traces out the rule of updated cases under different meteorological condition. Results: Our results show that the confirmed cases decreased by 0.390% (95% CI: -0.478 ~ -0.302) for increasing each one degree of TD if TD is less than 11.5°C; It will increase by 0.302% (95% CI: 0.215 ~ 0.388) for every 1°C increase in the TD (lag0-4) at the interval [11.5, 16]; Meanwhile the number of newly confirmed COVID-19 cases will increase by 0.321% (95% CI: 0.142 ~ 0.499) for every 1°C increase in the TD (lag0-4) when the TD (lag0-4) is over 16°C, and the most fluctuation occurred on Sunday. The results of the sensitivity analysis confirmed our model robust. Conclusions: In US, this interval effect of TD reminds us that it is urgent to control the spread and infection of COVID-19 when TD becomes greater in autumn and the ongoing winter.

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Section: Discussionmentioning

confidence: 94%

Non-linear link between temperature difference and COVID-19: Excluding the effect of population density

Ding

Gao

Shao³

2021

J Infect Dev Ctries

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“…Both SIR and SEIR models are used to predict the number of confirmed, susceptible, recovered cases, and deaths with the involvement of human mobility measures as variables or modeling parameters. Other studies employ the extended or modified SIR or SEIR models to improve the model performance (Ding and Gao 2020, Ngonghala, Iboi et al 2020, Sun, Wang et al 2020, Yang, Qi et al 2020, Zhang, Dong et al 2020), including SEIR with Quarantined, Dead, and Diagnosed (SEIR-QDD) model (Wang, Xu et al 2020), SIR branching process model (O’Sullivan, Gahegan et al 2020), SEIR-social distancing model (Gupta, Jain et al 2020), and a 14-compartment dynamic model (Westerhoff and Kolodkin 2020). The remaining articles utilize combined models integrating the classic SIR or SEIR models with other statistical models, including SEIR and network model (Peirlinck, Linka et al 2020), SEIR model combining mobility model (Linka, Peirlinck et al 2020), modified SEAIR model with optimization-based decision-making framework (Tsay, Lejarza et al 2020), SEPIA model (Gatto, Bertuzzo et al 2020), SEIR model based on travel networks (Lai, Ruktanonchai et al 2020), and generalized linear mixed regression model combining SIR model (Zhang, Litvinova et al 2020).…”

Section: Resultsmentioning

confidence: 99%

“…Specifically, these quantitative modeling work aim to quantify the COVID-19 pandemic by various mathematical/mechanistic state-space models as summarized in the modeling section (section 3.2), including epidemic models (Aviv-Sharon and Aharoni 2020, Ding and Gao 2020, Djurović 2020, Gatto, Bertuzzo et al 2020, Lai, Ruktanonchai et al 2020, Peirlinck, Linka et al 2020, Roda, Varughese et al 2020, Sirkeci and Yüceşahin 2020, Wang, Xu et al 2020, Zhao, Wang et al 2020), spatial-temporal models (Bherwani, Anjum et al 2020, Jia, Lu et al 2020, O’Sullivan, Gahegan et al 2020), biological models (Westerhoff and Kolodkin 2020), and other advanced mathematical models (Killeen and Kiware 2020, Tsay, Lejarza et al 2020, Yang, Qi et al 2020). The common characteristic of these modeling approaches involves the measures of human mobility and social restriction policies as parameters into the modeling configuration.…”

Section: Resultsmentioning

confidence: 99%

Human mobility and COVID-19 transmission: a systematic review and future directions

Zhang

Wang

et al. 2021

Preprint

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Without a widely distributed vaccine, controlling human mobility has been identified and promoted as the primary strategy to mitigate the transmission of COVID-19. Many studies have reported the relationship between human mobility and COVID-19 transmission by utilizing the spatial-temporal information of mobility data from various sources. To better understand the role of human mobility in the pandemic, we conducted a systematic review of articles that measure the relationship between human mobility and COVID-19 in terms of their data sources, statistical models, and key findings. Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we selected 47 articles from Web of Science Core Collection up to September 2020. Restricting human mobility reduced the transmission of COVID-19 spatially, although the effectiveness and stringency of policy implementation vary temporally and spatially across different stages of the pandemic. We call for prompt and sustainable measures to control the pandemic. We also recommend researchers 1) to enhance multi-disciplinary collaboration; 2) to adjust the implementation and stringency of mobility-control policies in corresponding to the rapid change of the pandemic; 3) to improve statistical models used in analyzing, simulating, and predicting the transmission of the disease; and 4) to enrich the source of mobility data to ensure data accuracy and suability.

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“…India [12][13], Italia [14], Meksiko [15], dan Indonesia [16][17]. Terdapat beberapa penelitian yang menggunakan kelompok individu laten sesuai dengan konsep masa inkubasi pada COVID- (1)…”

Section: Pendahuluanunclassified

Model Matematika COVID-19 dengan Sumber Daya Pengobatan yang Terbatas

Rifanti¹,

Dewi²,

Nurlaili³

et al. 2021

LIMITS

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Pada penelitian ini, kami menggunakan model kompartemen Susceptible-Exposed-Infected-Recovered (SEIR) untuk analisis dampak sumber daya pengobatan yang terbatas dan memprediksi dinamika penyebaran COVID-19 di Indonesia. Metode yang digunakan adalah penurunan angka rasio reproduksi dasar dan titik ekuilibrium menggunakan analisis sistem dinamik dalam bentuk persamaan diferensial non linier yang diperoleh dari model awal. Kemudian, kami menganalisis angka rasio reproduksi dasar dan titik ekuilibrium, serta memprediksi kondisi pandemi COVID-19 menggunakan kasus nyata di Indonesia sejak 2 Maret hingga 30 Nopember 2020. Dari hasil penelitian ini, diperoleh bahwa jika perubahan kasus terinfeksi ( ) terhadap waktu kurang dari 2640 kasus, maka angka rasio reproduksi dasar menjadi kurang dari nol dan nilai ( ) semakin mendekati nol saat mulai memasuki bulan Maret 2021. Hal tersebut berarti, jika ratarata kasus positif terkonfirmasi harian masih di bawah kapasitas maksimal sumber daya pengobatan, yaitu 2640 kasus, maka dari hasil analisis model diprediksikan bahwa penyakit akan mulai menghilang pada bulan Maret 2021. Sebaliknya, jika kasus positif terkonfirmasi harian di atas 2640 kasus, maka diperkirakan penyakit akan mulai menghilang pada Juni 2021. Kata Kunci: COVID-19, model kompartemen SEIR, pengobatan

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An Evaluation of COVID-19 in Italy: A data-driven modeling analysis

Cited by 7 publications

References 6 publications

Non-linear link between temperature difference and COVID-19: Excluding the effect of population density

Non-linear link between temperature difference and COVID-19: Excluding the effect of population density

Human mobility and COVID-19 transmission: a systematic review and future directions

Model Matematika COVID-19 dengan Sumber Daya Pengobatan yang Terbatas

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