Predicting the Spread of SARS-CoV-2 in Italian Regions: The Calabria Case Study, February 2020–March 2022

Branda, Francesco; Abenavoli, Ludovico; Pierini, Massimo; Mazzoli, Sandra

doi:10.3390/diseases10030038

Cited by 9 publications

(7 citation statements)

References 31 publications

(40 reference statements)

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“…Another important parameter that is closely related to infectivity is the effective regeneration number, which measures the ability to spread the disease [ 17 ]. It can be simply interpreted as the average number of people infected by an infected person during the infectious period [ 18 ].…”

Section: Experimental Simulationmentioning

confidence: 99%

All-People-Test-Based Methods for COVID-19 Infectious Disease Dynamics Simulation Model: Towards Citywide COVID Testing

Liu

Yang

Fong

et al. 2022

IJERPH

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The conversion rate between asymptomatic infections and reported/unreported symptomatic infections is a very sensitive parameter for model variables that spread COVID-19. This is important information for follow-up use in screening, prediction, prognostics, contact tracing, and drug development for the COVID-19 pandemic. The model described here suggests that there may not be enough researchers to solve all of these problems thoroughly and effectively, and it requires careful selection of what we are doing and rapid sharing of results and models and optimizing modeling simulations with value to reduce the impact of COVID-19. Exploring simulation modeling will help decision makers make the most informed decisions. In order to fight against the “Delta” virus, the establishment of a line of defense through all-people testing (APT) is not only an effective method summarized from past experience but also one of the best means to effectively cut the chain of epidemic transmission. The effect of large-scale testing has been fully verified in the international community. We developed a practical dynamic infectious disease model-SETPG (A + I) RD + APT by considering the effects of the all-people test (APT). The model is useful for studying effects of screening measures and providing a more realistic modelling with all-people-test strategies, which require everybody in a population to be tested for infection. In prior work, a total of 370 epidemic cases were collected. We collected three kinds of known cases: the cumulative number of daily incidences, daily cumulative recovery, and daily cumulative deaths in Hong Kong and the United States between 22 January 2020 and 13 November 2020 were simulated. In two essential strategies of the integrated SETPG (A + I) RD + APT model, comparing the cumulative number of screenings in derivative experiments based on daily detection capability and tracking system application rate, we evaluated the performance of the timespan required for the basic regeneration number (R0) and real-time regeneration number (R0t) to reach 1; the optimal policy of each experiment is available, and the screening effect is evaluated by screening performance indicators. with the binary encoding screening method, the number of screenings for the target population is 8667 in HK and 1,803,400 in the U.S., including 6067 asymptomatic cases in HK and 1,262,380 in the U.S. as well as 2599 cases of mild symptoms in HK and 541,020 in the U.S.; there were also 8.25 days of screening timespan in HK and 9.25 days of screening timespan required in the U.S. and a daily detectability of 625,000 cases in HK and 6,050,000 cases in the U.S. Using precise tracking technology, number of screenings for the target population is 6060 cases in HK and 1,766,420 cases in the U.S., including 4242 asymptomatic cases in HK and 1,236,494 cases in the U.S. as well as 1818 cases of mild symptoms in HK and 529,926 cases in the U.S. Total screening timespan (TS) is 8.25~9.25 days. According to the proposed infectious dynamics model that adapts to the all-people test, all of the epidemic cases were reported for fitting, and the result seemed more reasonable, and epidemic prediction became more accurate. It adapted to densely populated metropolises for APT on prevention.

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Section: Experimental Simulationmentioning

confidence: 99%

All-People-Test-Based Methods for COVID-19 Infectious Disease Dynamics Simulation Model: Towards Citywide COVID Testing

Liu

Yang

Fong

et al. 2022

IJERPH

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“…As outbreaks grow, there is a need to predict disease dynamics, estimate potential burden, and evaluate interventions (Branda et al, 2023). In the next steps, attention turns to estimating vaccine efficacy and monitoring outbreaks and evolutionary dynamics (Branda et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

The challenges of open data for future epidemic preparedness: The experience of the 2022 Ebolavirus outbreak in Uganda

et al. 2023

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On 20 September 2022, the Ministry of Health in Uganda, together with the World Health Organization—Regional Office for Africa (WHO AFRO) confirmed an outbreak of EVD due to Sudan ebolavirus in Mubende District, after one fatal case was confirmed. Real-time information are needed to provide crucial information to understand transmissibility, risk of geographical spread, routes of transmission, risk factors of infection, and provide the basis for epidemiological modelling that can inform response and containment planning to reduce the burden of disease. We made an effort to build a centralized repository of the Ebola virus cases from verified sources, providing information on dates of symptom onset, locations (aggregated to the district level), and when available, the gender and status of hospitals, reporting bed capacity and isolation unit occupancy rate according to the severity status of the patient. The proposed data repository provides researchers and policymakers timely, complete, and easy-accessible data to monitor the most recent trends of the Ebola outbreak in Ugandan districts with informative graphical outputs. This favors a rapid global response to the disease, enabling governments to prioritize and adjust their decisions quickly and effectively in response to the rapidly evolving emergency, with a solid data basis.

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“…Thus, there is a need to gain fundamental knowledge about the epidemiological characteristics of a new variant or infection, from transmission potential to natural history. [5][6][7][8] We spent the first days of 2023 in the "shadow of the dragon," with the fear of a China as the engine of a new wave of COVID-19, linked, perhaps, to unknown and mysterious variants that are more contagious and aggressive. A shadow that brings to mind what was already experienced 3 years ago, at the beginning of the pandemic.…”

mentioning

confidence: 99%

Is a new COVID‐19 wave coming from China due to an unknown variant of concern? Keep calm and look at the data

Branda

Scarpa

Ciccozzi

et al. 2023

Journal of Medical Virology

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Predicting the Spread of SARS-CoV-2 in Italian Regions: The Calabria Case Study, February 2020–March 2022

Cited by 9 publications

References 31 publications

All-People-Test-Based Methods for COVID-19 Infectious Disease Dynamics Simulation Model: Towards Citywide COVID Testing

All-People-Test-Based Methods for COVID-19 Infectious Disease Dynamics Simulation Model: Towards Citywide COVID Testing

The challenges of open data for future epidemic preparedness: The experience of the 2022 Ebolavirus outbreak in Uganda

Is a new COVID‐19 wave coming from China due to an unknown variant of concern? Keep calm and look at the data

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