Number Of Daily Infections Research Articles

Mathematical Modelling of Virus Spreading in COVID-19.

A mathematical model is proposed to analyze the spreading dynamics of COVID-19. By using the parameters of the model, namely the basic reproduction number (R0) and the attenuation constant (k), the daily number of infections (DNI) and the cumulative number of infections (CNI) over time (m) are deduced and shown to be in good agreement with experimental data. This model effectively addresses three key issues: (1) inferring the conditions under which virus infections die out for a specific strain given R0; (2) explaining the occurrence of second waves of infection and developing preventive measures; and (3) understanding the competitive spread of two viruses within a region and devising control strategies. The findings highlight the potential of this simple mathematical framework in comprehensively addressing these challenges. The theoretical insights derived from this model can guide the evaluation of infection wave severity and the formulation of effective strategies for controlling and mitigating epidemic outbreaks.

Editor’s Corner

Hello, and happy new year! What a ride the first year as DttP editor has been. Let’s take a quick look at just a few of the major events:GODORT celebrated its 50th anniversary.January 3—The number of daily infections of COVID-19 exceeded one million for the first time since the start of the pandemic.February 24—Russia launched a military invasion of Ukraine.

Impact of the COVID-19 Pandemic on the Level of Anxiety and Depression in Caregivers of Children Benefiting from Neurorehabilitation Services.

At the turn of March and April 2020, due to the occurrence of COVID-19 in Poland, the first restrictions on the provision of rehabilitation services were introduced. Nevertheless, caregivers strived to ensure that their children could benefit from rehabilitation services. To determine which of the selected data presented in the media reflecting the intensity of the COVID-19 epidemic in Poland differentiated the level of anxiety and depression in caregivers of children benefiting from neurorehabilitation services. The study group consisted of caregivers of children (n = 454) receiving various neurorehabilitation services in the inpatient ward of Neurological Rehabilitation of Children and Adolescents (n = 200, 44%), in the Neurorehabilitation Day Ward (n = 168, 37%), and in the Outpatient Clinic (n = 86, 19%) of the Clinical Regional Rehabilitation and Education Center in Rzeszow. The average age of the respondents was 37.23 ± 7.14 years. The Hospital Anxiety and Depression Scale (HADS) was used to measure the severity of anxiety and depression in caregivers of children. The questionnaires were distributed from June 2020 to April 2021. As a measure of the severity of the COVID-19 epidemic in Poland, the figures presented in the media were adopted. In addition, data on the COVID-19 pandemic presented in the media (Wikipedia, TVP Info, Polsat Nes, Radio Zet) on the day preceding the completion of the survey were analyzed based on statistical analysis methods. 73 of the surveyed caregivers (16.08%) suffered from severe anxiety disorders, and 21 (4.63%) from severe depressive disorders. The average severity of anxiety (HADS) in the subjects was 6.37 points, and the average severity of depression was 4.09 points. There was no statistically significant relationship between the data presented in the media-such as daily number of infections, total number of infections, daily number of deaths, total number of deaths, total number of recoveries, number of hospitalizations, and people under quarantine-and the level of anxiety and depression of the studied caregivers (p > 0.05). It was not found that the selected data presented in the media, showing the intensity of the COVID-19 epidemic in Poland, significantly differentiated the level of anxiety and depression among caregivers of children using neurorehabilitation services. Their motivation to continue the treatment, caused by concern for their children's health, resulted in less severe symptoms of anxiety and depression during the peak period of the COVID-19 pandemic.

Modelling the dynamics of infection, waning of immunity and re-infection with the Omicron variant of SARS-CoV-2 in Aotearoa New Zealand

Aotearoa New Zealand experienced a wave of the Omicron variant of SARS-CoV-2 in 2022 with around 200 confirmed cases per 1000 people between January and May. Waning of infection-derived immunity means people become increasingly susceptible to re-infection with SARS-CoV-2 over time. We investigated a model that included waning of vaccine-derived and infection-derived immunity under scenarios representing different levels of behavioural change relative to the first Omicron wave. Because the durability of infection-derived immunity is a key uncertainty in epidemiological models, we investigated outcomes under different assumptions about the speed of waning. The model was used to provide scenarios to the New Zealand Government, helping to inform policy response and healthcare system preparedness ahead of the winter respiratory illness season. In all scenarios investigated, a second Omicron wave was projected to occur in the second half of 2022. The timing of the peak depended primarily on the speed of waning and was typically between August and November. The peak number of daily infections in the second Omicron wave was smaller than in the first Omicron wave. Peak hospital occupancy was also generally lower than in the first wave but was sensitive to the age distribution of infections. A scenario with increased contact rates in older groups had higher peak hospital occupancy than the first wave. Scenarios with relatively high transmission, whether a result of relaxation of control measures or voluntary behaviour change, did not necessarily lead to higher peaks. However, they generally resulted in more sustained healthcare demand (>250 hospital beds throughout the winter period). The estimated health burden of Covid-19 in the medium term is sensitive to the strength and durability of infection-derived and hybrid immunity against reinfection and severe illness, which are uncertain.

Ascertainment rate of SARS-CoV-2 infections from healthcare and community testing in the UK

The proportion of SARS-CoV-2 infections ascertained through healthcare and community testing is generally unknown and expected to vary depending on natural factors and changes in test-seeking behaviour. Here we use population surveillance data and reported daily case numbers in the United Kingdom to estimate the rate of case ascertainment. We mathematically describe the relationship between the ascertainment rate, the daily number of reported cases, population prevalence, and the sensitivity of PCR and Lateral Flow tests as a function time since exposure. Applying this model to the data, we estimate that 20%–40% of SARS-CoV-2 infections in the UK were ascertained with a positive test with results varying by time and region. Cases of the Alpha variant were ascertained at a higher rate than the wild type variants circulating in the early pandemic, and higher again for the Delta variant and Omicron BA.1 sub-lineage, but lower for the BA.2 sub-lineage. Case ascertainment was higher in adults than in children. We further estimate the daily number of infections and compare this to mortality data to estimate that the infection fatality rate increased by a factor of 3 during the period dominated by the Alpha variant, and declined in line with the distribution of vaccines. This manuscript was submitted as part of a theme issue on “Modelling COVID-19 and Preparedness for Future Pandemics”.

Prediction of COVID-19 Infections for Municipalities in the Netherlands: Algorithm Development and Interpretation.

BackgroundCOVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities.ObjectiveWe aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques.MethodsWe collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands.ResultsThe final prediction model had an R2 of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters <10 μm (PM10) in the air, the percentage of Labour party voters, and the number of children in a household.ConclusionsData about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared.

Forecasting the spread of the COVID-19 pandemic in Kenya using SEIR and ARIMA models.

COVID-19, a coronavirus disease 2019, is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first case in Kenya was identified on March 13, 2020, with the pandemic increasing to about 237,000 confirmed cases and 4,746 deaths by August 2021. We developed an SEIR model forecasting the COVID-19 pandemic in Kenya using an Autoregressive Integrated moving averages (ARIMA) model. The average time difference between the peaks of wave 1 to wave 4 was observed to be about 130 days. The 4th wave was observed to have had the least number of daily cases at the peak. According to the forecasts made for the next 60 days, the pandemic is expected to continue for a while. The 4th wave peaked on August 26, 2021 (498th day). By October 26, 2021 (60th day), the average number of daily infections will be 454 new cases and 40 severe cases, which would require hospitalization, and 16 critically ill cases requiring intensive care unit services. The findings of this study are key in developing informed mitigation strategies to ensure that the pandemic is contained and inform the preparedness of policymakers and health care workers.

SARS-CoV-2 Kappa Variant Shows Pathogenicity in a Syrian Hamster Model.

Objectives: The emergence of SARS-CoV-2 lineage B.1.617 variants in India has been associated with a surge in the number of daily infections. We investigated the pathogenic potential of Kappa (B.1.617.1) variant in Syrian golden hamsters. Methods: Two groups of Syrian golden hamsters (18 each) were inoculated intranasally with SARS-CoV-2 isolates, B.1 (D614G) and Kappa variant, respectively. The animals were monitored daily for the clinical signs and body weight. Throat swab, nasal wash, and organ samples (lungs, nasal turbinate, trachea) were collected and screened using SARS-CoV-2-specific RT-qPCR. Histopathologic evaluation of the lung samples was performed. Results: The hamsters infected with the Kappa variant demonstrated increased body weight loss compared to the B.1 lineage isolate. The highest viral RNA load was observed in the nasal turbinate and lung specimens of animals infected with both variants. A significantly higher sgRNA load was observed in the nasal swabs (7 DPI), trachea (3 DPI), and lungs (3 DPI) of hamsters infected with the Kappa variant. Neutralizing antibody response generated in the B.1 lineage-infected hamster sera were comparable against both B.1 and Kappa variant in contrast to Kappa variant-infected hamsters, which showed lower titers against B.1 lineage isolate. Gross and microscopic evaluation of the lung specimens showed severe lung lesions in hamsters infected with Kappa variant compared to B.1. Conclusions: The study demonstrates pathogenicity of Kappa variant in hamsters evident with reduced body weight, high viral RNA load in lungs, and pronounced lung lesions. Both Kappa variant- and B.1-infected hamsters produced neutralizing antibodies against both variants studied.

The Exponentiated Gumbel-Weibull {Logistic} Distribution with Application to Nigeria's COVID-19 Infections Data.

A new flexible univariate probability distribution was defined in this paper. The new distribution is so called the ‘exponentiated Gumbel–Weibull {logistic} distribution’ and it arose by using the exponentiated Gumbel distribution to generate a generalized Weibull distribution using the logit function or the quantile function of the logistic distribution as a link. The new distribution was observed to be both unimodal and bimodal as well as exhibits various shape and tail properties consistent with data arising from several real life phenomena. A detail study of its statistical properties was carried out and the maximum likelihood method was used in the estimation of its parameters. The new distribution was applied in fitting the reported daily number of infections due to the COVID-19 pandemic in Nigeria. Five other datasets were further used to ascertain the flexibility of the new distribution in fitting data sets with different statistical properties.

Concept Drift in Japanese COVID-19 Infection Data

In this study, we analyze concept drifts in the daily infection data of COVID-19 in Japan. A lockdown, the spread of vaccines, and the emergence of new variants of COVID-19 have had a significant impact on the number of daily infections. These changes, also known as concept drifts, make the prediction of COVID-19 infection rates difficult. Because the prediction of infection trends is crucial to protect people from the disease, this study aims to generate accurate predictions by handling concept drifts in the trend data. The key concept behind this method is a brute-force tuning of the training period. Although prior studies tended to require pre-tuned parameters to locate the drift points, this can be avoided through brute-force tuning. Experimental results show significant improvements in prediction accuracy. Furthermore, the extracted points where concept drifts occur appear to correspond to new COVID-19 variants and other important state changes.

Models to Predict the Number of Infected Cases and Deaths from COVID‐19 in Chile and Its Most Affected Regions

This paper designs and implements a methodology to model the evolution of the COVID‐19 pandemic, produced by the SARS‐CoV‐2 virus, in what was called the first wave in Chile, which lasted from March 2 to 31 October 2020. The models are based on sigmoidal growth curves and can be used to predict the number of daily infections and deaths in future days, making them a useful tool for sanitary authorities to manage an epidemic. The methodology is applied to the entire country and to each of its most affected regions. In addition, the dynamics of these models allow it to be nurtured with the new information that is being produced and forecast a tentative date on which there would be some control over the pandemic. Moreover, these models allow for predicting the total number of infected and deceased people at the time the pandemic is under control. However, the simplicity of these models, which consider only the accumulated data of those infected and deceased, does not contemplate an intervention analysis such as vaccinations, which, as is known, are being effective in controlling the pandemic.

Civil-Military Cooperation: Integrated Logistics in Response to the COVID-19 Crisis

Background: This article addresses the civil-military cooperation in response to the COVID-19 crisis. In early 2021, the number of daily infections and the growing number of COVID-19 hospitalizations in Portugal were dramatic, reaching the highest rates in the world. In response, the Armed Forces were engaged and played a significant role in helping the National Health Service (NHS). The NHS is just one of many existing examples which demonstrate the relevance of logistical support from the Armed Forces so that civil institutions can return to a sustainable and autonomous situation. Methods: In order to understand and describe this real-life phenomenon, a qualitative and exploratory case study was conducted. Several sources of data collection were used for triangulation and corroboration. Results: The results suggest that, in emergency situations, logistical operations of the Armed Forces in support of civil society are divided into four major areas: planning and organizational activities; priority setting; combat service support; and training and self-reliance. Conclusions: Once the four areas of action of the Armed Forces were identified, the response to the COVID-19 crisis became more evident, as civil entities were able to make requests suited to military capabilities.

Non-pharmaceutical interventions, vaccination, and the SARS-CoV-2 delta variant in England: a mathematical modelling study

SummaryBackgroundEngland's COVID-19 roadmap out of lockdown policy set out the timeline and conditions for the stepwise lifting of non-pharmaceutical interventions (NPIs) as vaccination roll-out continued, with step one starting on March 8, 2021. In this study, we assess the roadmap, the impact of the delta (B.1.617.2) variant of SARS-CoV-2, and potential future epidemic trajectories.MethodsThis mathematical modelling study was done to assess the UK Government's four-step process to easing lockdown restrictions in England, UK. We extended a previously described model of SARS-CoV-2 transmission to incorporate vaccination and multi-strain dynamics to explicitly capture the emergence of the delta variant. We calibrated the model to English surveillance data, including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data using a Bayesian evidence synthesis framework, then modelled the potential trajectory of the epidemic for a range of different schedules for relaxing NPIs. We estimated the resulting number of daily infections and hospital admissions, and daily and cumulative deaths. Three scenarios spanning a range of optimistic to pessimistic vaccine effectiveness, waning natural immunity, and cross-protection from previous infections were investigated. We also considered three levels of mixing after the lifting of restrictions.FindingsThe roadmap policy was successful in offsetting the increased transmission resulting from lifting NPIs starting on March 8, 2021, with increasing population immunity through vaccination. However, because of the emergence of the delta variant, with an estimated transmission advantage of 76% (95% credible interval [95% CrI] 69–83) over alpha, fully lifting NPIs on June 21, 2021, as originally planned might have led to 3900 (95% CrI 1500–5700) peak daily hospital admissions under our central parameter scenario. Delaying until July 19, 2021, reduced peak hospital admissions by three fold to 1400 (95% CrI 700–1700) per day. There was substantial uncertainty in the epidemic trajectory, with particular sensitivity to the transmissibility of delta, level of mixing, and estimates of vaccine effectiveness.InterpretationOur findings show that the risk of a large wave of COVID-19 hospital admissions resulting from lifting NPIs can be substantially mitigated if the timing of NPI relaxation is carefully balanced against vaccination coverage. However, with the delta variant, it might not be possible to fully lift NPIs without a third wave of hospital admissions and deaths, even if vaccination coverage is high. Variants of concern, their transmissibility, vaccine uptake, and vaccine effectiveness must be carefully monitored as countries relax pandemic control measures.FundingNational Institute for Health Research, UK Medical Research Council, Wellcome Trust, and UK Foreign, Commonwealth and Development Office.

Pandemic 2.0 – Where Do We Go From Here? The Delta Variant and the Young.

With the Delta variant becoming dominant globally, we need to change how we think about the properties and destructive potential of new virus variants. We need good policy decisions to ensure sufficient protection of our unprotected young, for health and for the economy. Throughout the pandemic, the daily number of infections, hospitalizations, and deaths from … Continued

Potential Contributors to Increased Pulmonary Embolism Hospitalizations During the COVID-19 Pandemic: Insights From the German-Wide Helios Hospital Network.

Background: After the first COVID-19 infection wave, a constant increase of pulmonary embolism (PE) hospitalizations not linked with active PCR-confirmed COVID-19 was observed, but potential contributors to this observation are unclear. Therefore, we analyzed associations between changes in PE hospitalizations and (1) the incidence of non-COVID-19 pneumonia, (2) the use of computed tomography pulmonary angiography (CTPA), (3) volume depletion, and (4) preceding COVID-19 infection numbers in Germany.Methods: Claims data of Helios hospitals in Germany were used, and consecutive cases with a hospital admission between May 6 and December 15, 2020 (PE surplus period), were analyzed and compared to corresponding periods covering the same weeks in 2016–2019 (control period). We analyzed the number of PE cases in the target period with multivariable Poisson general linear mixed models (GLMM) including (a) cohorts of 2020 versus 2016–2019, (b) the number of cases with pneumonia, (c) CTPA, and (d) volume depletion and adjusted for age and sex. In order to associate the daily number of PE cases in 2020 with the number of preceding SARS-CoV-2 infections in Germany, we calculated the average number of daily infections (divided by 10,000) occurring between 14 up to 90 days with increasing window sizes before PE cases and modeled the data with Poisson regression.Results: There were 2,404 PE hospitalizations between May 6 and December 15, 2020, as opposed to 2,112–2,236 (total 8,717) in the corresponding 2016–2019 control periods (crude rate ratio [CRR] 1.10, 95% CI 1.05–1.15, P < 0.01). With the use of multivariable Poisson GLMM adjusted for age, sex, and volume depletion, PE cases were significantly associated with the number of cases with pneumonia (CRR 1.09, 95% CI 1.07–1.10, P < 0.01) and with CTPA (CRR 1.10, 95% CI 1.09–1.10, P < 0.01). The increase of PE cases in 2020 compared with the control period remained significant (CRR 1.07, 95% CI 1.02–1.12, P < 0.01) when controlling for those factors. In the 2020 cohort, the number of preceding average daily COVID-19 infections was associated with increased PE case incidence in all investigated windows, i.e., including preceding infections from 14 to 90 days. The best model (log likelihood −576) was with a window size of 4 days, i.e., average COVID-19 infections 14–17 days before PE hospitalization had a risk of 1.20 (95% CI 1.12–1.29, P < 0.01).Conclusions: There is an increase in PE cases since early May 2020 compared to corresponding periods in 2016–2019. This surplus was significant even when controlling for changes in potential modulators such as demographics, volume depletion, non-COVID-19 pneumonia, CTPA use, and preceding COVID-19 infections. Future studies are needed (1) to investigate a potential causal link for increased risk of delayed PE with preceding SARS-CoV-2 infection and (2) to define optimal screening for SARS-CoV-2 in patients presenting with pneumonia and PE.

The impact of social distancing on COVID-19 infections and deaths

BackgroundTo assess the impact of the social isolation index on the number of infections and deaths by COVID-19 in the state of São Paulo (Brazil).MethodsDaily isolation data, obtained through geolocation information by mobile phone, were evaluated together with the number of daily infections and deaths by COVID-19 in the state of São Paulo. The study was conducted from February 26 to May 19, 2020. The data were modeled through the vector autoregression (VAR) model.ResultsThe isolation index has an effect of approximately 5% in variation in the number of infections, and 7% in the number of deaths. The impulse response function (IRF) caused a drop of 0.15% in the number of new cases/day, and 0.17% in the number of deaths/day following a shock in the isolation index. For both cases, this effect occurred 1 day after the shock and stabilized after 10 periods. An increase of 1% in the isolation index led to a reduction of 6.91% in new cases and 6.90% in the number of deaths. The 30 cumulative day reduction reached 22.72% in terms of transmission and 35.39% for deaths.ConclusionsThe social isolation index is related to deaths and infections from SARS-CoV-2. Although distancing measures are accompanied with impacts on the economy and the emergence of other morbidities, the benefits caused by the reduction in the speed of contagion are significant. The adoption of distancing measures has a substantial impact on the number of infected individuals and deaths by COVID-19.

SARS CoV2 Second Wave Onslaught in India and Mass Vaccination Prospects

In little over a year, in March 2020 the World Health Organisation declared COVID-19 a pandemic and on March 24, India went into the longest lockdown. On March 25, 2020, the number of new cases stood 121 and deaths were two. Contrast to that, in corresponding month this year, there are more than 53,000 cases daily and more than 250 deaths. It is still rising, doubling every 12-14 days. India is now the country with the highest number of daily infections and casualties. Unlike the first wave, the cases are currently much more widespread and has severely strained the country’s critical care capacity. This paper attempts to note the virus onslaught during Covid-19 second wave and India’s prospect at navigating the pandemic and foster for citizens early and reliable access to vaccines.

Otoi-NARIMA Model for forecast seasonality of COVID-19 waves: Case of Kenya

Background: Kenya has experienced three COVID-19 waves which left authorities mandated to do disease surveillance and estimate the burden of disease in a complex and uncertain environment with citizens’ trust in institutions wavering having lost jobs and incomes. The citizens’ vulnerability worsened with inability to connect to social support when each household wellbeing and financial ability came under threat causing much anxiety about the future. Mathematical modelling of the spread of disease informs surveillance, planning, budgeting, and response to save lives and livelihoods. In that regard, accuracy of predictions and forecasts is highly desirable. The length of duration of COVID-19 waves, the likely start and end dates, and the number of daily infections need to be estimated with precision. These inform and provide a window for authorities working in a holistic and integrated manner with researchers and experts to protect people especially the most vulnerable populations and communities to fully acquire WHO approved vaccines before the subsequent forecasted period of COVID-19 waves. Method: Globally COVID-19 has serious health crisis with 134 million cases and 2.9 million deaths as of April 9, 2021. Kenya has experienced 392 days of COVID-19 with 136, 893 infections. The infections vary from county to county. Daily case infections data between March 13, 2020 and April 3, 2021 is used. The data is tested for stationarity and cointegration using ADF and Johansen Cointegration tests respectively. The normalized series is equally taken through these tests. A moving average of the Daily cases is estimated. The normalized series is superimposed on the moving averages. Then the combined series are used to construct Otoi-NARIMA model. The resulting model’s residual is tested for autocorrelation using autocorrelation function (ACF) and partial autocorrelation function (PAFC) tests. Also, validity of the model is tested using Ljung-Box test. The model is used to forecast 45 daily cased from April 4, 2021 to May 18, 2021. The forecasted results are visualized. Likely dates for end of third wave and potential beginning of fourth wave are picked from visualization and output of Otoi-NARIMA model. The results are compared with results of standard ARIMA model. Results: The series and normalized version are I(1) stationary. Johansen Cointegration test revealed the existence of one cointegration rank between the series. Implication is that the series and superimposed normalized version would not drift apart overtime when used to estimate Otoi-NARIMA model. Whereas ACF revealed that both models show no autocorrelation PACF was inconclusive. On validity, the Ljung-Box test showed that both Otoi-NARIMA and standard ARIMA are valid, however the former is superior. The Otoi-NARIMA model has distinctly identified the seasonality of COVID-19 waves. In terms of visualization clarity Otoi-model provides restricted forecasts. There is likelihood of Kenya’s third wave beginning to decline briefly between April 29, 2021 and May 9, 2021. Based on assumption that Kenya will not have fully vaccinated 51 in 100 people the wave is likely to continue between June 2, 2021 and after July 10, 2021 or the third wave will continue up to June 26, 2021, which is the likely peak. It is recommended that Kenya should aim to vaccinate 51 in 100 people before June 2, 2021.

THE SCALE OF THE SPREAD OF COVID-19 IN GEORGIA AND EFFECTIVENESS OF PREVENTIVE MEASURES IMPLIMENTED BY THE GOVERNMENT – WHAT DO MODELS SAY?

. In this article we present forecasts of the spread of COVID-19 virus, obtained by econometric and machine learning methods. Furthermore, by employing modelling method, we estimate effectiveness of preventive measures implemented by the government. Each of the models discussed in this article is modelling different characteristics of the COVID-19 epidemic’s trajectory: peak and end date, number of daily infections over different forecasting horizons, total number of infection cases. All these provide quite clear picture to the interested reader of the future threats posed by COVID-19. In terms of existing models and data, our research indicates that phenomenological models do well in forecasting the trend, duration and total infections of the COVID- 19 epidemic, but make serious mistakes in forecasting the number of daily infections. Machine learning models, deliver more accurate short –term forecast of daily infections, but due to data limitations, they struggle to make long-term forecasts. Compartmental models are the best choice for modelling the measures implemented by the government for preventing the spread of COVID-19 and determining optimal level of restrictions. These models show that until achieving herd immunity (i.e. without any epidemiological or government implemented measures), approximate number of people infected with COVID-19 would be 3 million, but due to preventive measures, expected total number of infections has reduced to several thousand (1555-3189) people. This unequivocally indicates the effectiveness of the preventive measures.

Suppression of Groups Intermingling as an Appealing Option for Flattening and Delaying the Epidemiological Curve While Allowing Economic and Social Life at a Bearable Level during the COVID-19 Pandemic.

The COVID‐19 pandemic in a population modelled as a network wherein infection can propagate both via intra‐ and inter‐group interactions is simulated. The results emphasize the importance of diminishing the inter‐group infections in the effort of substantial flattening/delaying of the epi(demiologic) curve with concomitant mitigation of disastrous economy and social consequences. To exemplify, splitting a population into m (say, 5 or 10) noninteracting groups while keeping intra‐group interaction unchanged yields a stretched epidemiological curve having the maximum number of daily infections reduced and postponed in time by the same factor m (5 or 10). More generally, the study suggests a practical approach to fight against SARS‐ CoV‐ 2 virus spread based on population splitting into groups and minimizing intermingling between them. This strategy can be pursued by large‐scale infrastructure reorganization of activity at different levels in big logistic units (e.g., large productive networks, factories, enterprises, warehouses, schools, (seasonal) harvest work). Importantly, unlike total lockdown, the proposed approach prevents economic ruin and keeps social life at a more bearable level than distancing everyone from anyone. The declaration for the first time in Europe that COVID‐19 epidemic ended in the two‐million population Slovenia may be taken as support for the strategy proposed here.