Skip to main content
  • 7 Nikola Pašić Sq., 4th Fl., 11000 Belgrade
  • info@smj.rs

logo LKS

Review article

Modeling as an approach to pandemic uncertainty management: Mortality assessment of the COVID-19 pandemic

Aleksandar Stevanović1,2, Milena Šantrić Milićević1,2
  • Institute of Social Medicine, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
  • Center-School of Public Health and Health Managemen, University of Belgrade, Belgrade, Serbia

ABSTRACT

The progression of the COVID-19 pandemic has urged the medical and scientific community to attempt to model and predict the transmission of SARS-CoV-2. Forecasting the possible course of the COVID-19 pandemic has helped us to better understand the nuances in the effects of the adopted policy measures and has directed us towards future actions which may need to be undertaken. In this article, we briefly summarize several selected projection approaches used for estimating COVID-19 effects globally and locally (Serbia).


BACKGROUND

As the available knowledge grew on the novel coronavirus being the cause of mild, but also severe infections with the possible presence of asymptomatic carriers in the general population, so did the necessity for urgent action in order to prevent suffering, disabilities and economic loss [1],[2],[3],[4],[5]. Possible benefits of the implemented mitigation measures for the COVID-19 pandemic are assessed not only by their effects on the course of the pandemic, but also by estimating their direct and indirect economic and social repercussions [6],[7].

The progression of the COVID-19 pandemic has challenged many professional planners to try to model the current and future transmission of the novel virus - SARS-CoV-2, in order to achieve a better understanding of its effects in different settings. Epidemiological, political, regulatory, and socio-behavioral changes, as well as increasing knowledge and data, have been used to create different assumptions and flexible estimates of different population exposures to the virus, treatment rates, and outcomes. Projection approaches are constantly evolving using updated data to improve the models [6],[7]. Mandatory implementation of various public health measures against COVID-19, as well as support and containment measures are decisions made by state officials, primarily imposed in order to protect people’s health and lives [8],[9]. For instance, regulations regarding social distancing and the use of protective face masks were made based on the transmission mode of the virus variant, mobility and population density, as well as the overall public response to the pandemic. Rapid accumulation of data has been used for evaluation and monitoring of various epidemiological and clinical indicators, disaggregated by age, sex and other important determinants of health.

In this article we briefly summarize several selected projection approaches used for the estimation of COVID-19 effects globally and locally (e.g., mortality in Serbia).

ASSESSMENT OF THE COVID-19 GLOBAL AND LOCAL EFFECTS

Several models have estimated the COVID-19 effects, both globally and locally, leading to important recommendations from various stakeholders. Friedman et al. have noted that, aside from the attributes of the virus itself and characteristics of the location where it is being spread, the trajectory of the COVID-19 epidemic is notably influenced by the state-imposed mitigation measures and our individual compliance to those measures. The Institute for Health Metrics and Evaluation (IHME) has provided multi-level (global, regional, national and subnational) estimations of excess mortality due to COVID-19 [10]. Excess mortality is explained as the difference between the number of reported deaths or the mortality rates and the expected mortality numbers/rates, thus providing insight into the COVID-19 burden, in all countries. Knowing that confirmed COVID-19 deaths depend directly on the country’s capacity to test over time, it is possible to underestimate this number. For example, this is true for the patients dying in long-term care facilities. IHME estimates the proportion of excess mortality resulting from excess death due to COVID-19, also considering other drivers that affect excess mortality, which have not been verified yet (e.g., delayed health care during the pandemic, increase in mental disorders, alcohol and opiate use, reduction of mortality due to injuries, viruses and cardiovascular and chronic respiratory diseases). In their models, the excess COVID-19 mortality is a logit function of the infection-detection rate and context specifics. Ratios of excess COVID-19 deaths to reported COVID-19 deaths, by May 2021, range from very high levels, in many Eastern European and Central Asian countries, to ratios with low significance in several high-income countries. Globally, the cumulative excess COVID-19 death rate is 91.7 per 100,000. The top five countries with the highest estimated death rates per 100,000 due to COVID-19, as well as excess COVID-19 death rates in the period from March 2020 to May 2021, are the following: Azerbaijan (672.7 and 46.4, respectively), Bosnia and Herzegovina (601.1 and 268.3, respectively), Bulgaria (559.9 and 245.5, respectively), Albania (528.9 and 88.5, respectively), and Mexico (497.8 and 175.6, respectively) [10]. These estimates will be revised to take into account new information, in particular other COVID-19 burden drivers.

The 386 models have demonstrated good performance in providing global COVID-19 predictions of the time of maximum daily mortality [6]. The forecast-based mortality scenarios use various seasonality assumptions and incorporate different stages of the epidemic per country (some in stabilized ongoing transmission dynamics, others with a completely different epidemic trajectory), complex models of human behavior and government interventions. These model data and codes are publicly available and can be used to compare global, international, and national predictions and assess future performance predictions [11].

A recent evaluation included seven models of the magnitude of COVID-19 mortality. These models were made by IHME [10], DELPHI-MIT [12], Imperial College London [13], Youyang Gu [14], The Los Alamos National Laboratory [15], and the SIKJ-Alpha model from the USC Data Science Lab [16]. Overall, the best-performing model varied by region, however, the highest predicting errors were seen among countries in the Southern Hemisphere followed by those in the Northern Hemisphere (Eastern Europe and Central Asia and high-income countries). Comparison of the peak daily mortality timing showed that longer forecasts can yield errors in estimates from 1.0% at 1 week to 26.9% at 12 weeks of extrapolation.

Another model used by the IHME COVID-19 forecasting team was the SEIR model, which identified possible routes of the infection caused by the SARS-CoV-2 virus, in the United States, during a six-month period, in relation to the effects of non-pharmaceutical interventions [6]. This SEIR model used a database of deaths, recovered, infected, exposed and susceptible COVID-19 cases, spanning over a period of eight months. The model adjusted the projections for covariates such as test rates, mobility, seasonality of pneumonia, and use of masks per capita. For each state, the significant number of potential lives which could be saved by certain mandates of social distancing has been estimated. In many states, the use of protective face masks in public settings might be sufficient to mitigate the worst effects of the re-emergence of the epidemic [6].

COVID-19 IN SERBIA

Serbian Law on Protection of the Population Against Communicable Diseases recognizes the Institute of Public Health of Serbia Dr Milan Jovanović Batut (IPHOS) as the central institution for health surveillance and data collection [17]. Since the beginning of the pandemic, IPHOS has been publishing daily reports on the epidemiological situation in the country. The reports are disseminated through various media channels and interpreted by government officials and the Ministry of Health.

Based on the surveillance database of the IPHOS [18], in the period from the first registered case of COVID-19 (March 6, 2020) until the present day (July 29, 2021), the cumulative number of tested persons is 4,653,071, while the total number of confirmed cases is 721,267. In the same period, the greatest number of daily confirmed cases was 7,999, on December 1, 2020, the greatest number of daily confirmed deaths was 69, on December 4, 2020, and the greatest number of hospitalized COVID-19 patients was 9,728 on December 28, 2020 [18].

Before this article was submitted, the latest data was reported on July 29, 2021, which showed 292 new confirmed cases [18]. Also, clinical manifestations of COVID-19 have been continuously monitored, showing that the greatest number of patients requiring mechanical ventilation was 353, on December 17, 2020, while recently (on July 29, 2021) 13 people required mechanical ventilation, and there were zero confirmed COVID-19 deaths [18]. The cumulative number of confirmed COVID-19 deaths is 7,110, according to IPHOS, but this number does not include unconfirmed cases. The pandemic has affected the mortality patterns in Serbia. On July 26, 2021, the Statistical Office of the Republic of Serbia (SORS) issued an official release on live births and deaths in the period of January to June 2021 [19]. According to their data, the number of deaths in this period amounted to 65,817, which represents an increase of 14,248 (+27.8%) as compared to 2020 [20]. As stated earlier, this increase in the reported number of deaths, as compared to the expected mortality numbers, can simply be called excess mortality.

If we observe the excess mortality in the period from January to June 2021, in the four main geographical regions of Serbia, West Serbia leads with an increase of 30.5%, followed by the Region of Belgrade (+28.6%), Vojvodina (+24.9%) and East and South Serbia (+23,7%) [19].

However, it is important to emphasize, once again, that excess mortality is not simply equal to the cumulative number of COVID-19 related deaths. To estimate the cumulative number of COVID-19 deaths, many factors, which influence the dynamics of excess mortality during the pandemic, have to be taken into consideration. Some of these factors decrease the expected mortality, due to the reduction in mobility and social distancing. For example, there is a reduction in mortality due to a decrease in injuries or reduced transmission of other viruses. On the other hand, excess mortality can increase due to an increase in mental health disorders or inadequate management of chronic non-communicable diseases [10]. Up to this point, December 2020 was the month with the highest excess mortality during the epidemic in Serbia, according to the Statistical Office. If we look at the preliminary data from the official statistical report for December 2020, the registered number of deaths was 17,321, as compared to 8,803 in December 2019, which is an increase of 8,518 (+96.8%) [20]. It is important to note that the SORS calculates excess mortality by comparing the number of deaths between two consecutive years. Limitations to this approach are evident in case of phenomena which have a long-term demographic impact. In that sense, when evaluating short- and long-term impacts of an epidemic, different statistical approaches should be used to avoid under- or overestimating its effects.

Finally, if we calculate the cumulative excess mortality due to the epidemic in Serbia, according to the official data, that number amounts to 14,628 in 2020 (from March to December) and 14,248, thus far in 2021 (from January to June) making a grand total of 28,876 [19],[20]. Up to this moment, the Serbian Ministry of Health has issued just one official revision of the COVID-19 mortality numbers, June 30, 2021, confirming 10,356 COVID-19 related deaths.

THE WAY FORWARD

Hundreds of forecasting models have been published and/or publicly released, and it is often not immediately clear which models have had the best performance or are most appropriate for predicting a given aspect of the pandemic. Existing COVID-19 prediction models differ significantly in methodology, assumptions, prediction range, and estimated values, even in shortterm estimates for the same site. Therefore, it is important to emphasize that the greatest benefit of modeling is the social impact it generates. Ideally, decision makers will base their actions on the applied models, therefore potentially affecting thousands of lives, so the models must first be assessed with special statistical tools. Only verified models, those proven to be valid and credible, provide real insight into the various effects of the pandemic [21].

In this context, useful modeling insights should be accompanied by cohort studies reporting years of lives saved through the combined effects of public health measures and vaccine effectiveness, during the COVID-19 pandemic. Wide uncertainty intervals that accompany assessments made for longer extrapolation of COVID-19 related mortality and their peaks suggest the adoption of a health systems strategic approach for decision-making based on continuous information, verification and updates of the models.

Modeling the COVID-19 global and local effects is of immense public value, as public health facilities must prepare for potential urgent needs for workers, supplies and capacities. For instance, hospitals need to make strategic decisions regarding long ranging resources such as health workers, beds, intensive care beds and ventilators [6]. Similarly, the effects of COVID-19 on the economy and education sector need to be predicted [22], in order to reduce consequent unemployment and poverty [23].

CONCLUSION

Given the complexity of predicting human behavior and political decisions, the abovementioned modelling efforts are providing consistent direction of projections trajectories. Due to wide uncertainty intervals, joint action of various stakeholders is required for continuous improvement of the models’ power and credibility.

ABBREVIATIONS AND ACRONYMS

COVID-19 - coronavirus disease of 2019
GBD - Global Burden of Disease
IHME – Institute for Health Metrics and Evaluation
IPHOS – Institute of Public Health of Serbia
MIT – Massachusetts Institute of Technology
SARS-CoV-2 – Severe Acute Respiratory Syndrome Coronavirus 2
SORS – Statistical Office of the Republic of Serbia
USC – University of San Francisco

  • Conflict of interest:
    None declared.

Informations

Volume 2 No 3

September 2021

Pages 278-285
  • Keywords:
    COVID-19, pandemic, forecasting, policy measures
  • Received:
    03 September 2021
  • Revised:
    14 September 2021
  • Accepted:
    16 September 2021
  • Online first:
    30 September 2021
  • DOI:
  • Cite this article:
    Stevanović A, Šantrić-Milićević M. Modeling as an approach to pandemic uncertainty management: Mortality assessment of the COVID-19 pandemic. Serbian Journal of the Medical Chamber. 2021;2(3):278-85. doi: 10.5937/smclk2-33796
Corresponding author

Aleksandar Stevanović
Institute of Social Medicine, Faculty of Medicine, University of Belgrade
15 Dr Subotića Street, 11130 Belgrade, Serbia
E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.


  • 1. Davies NG, Klepac P, Liu Y, Prem K, Jit M, Eggo RM. Age-dependent effects in the transmission and control of Covid-19 epidemics. Nature Medicine. 2020;26:1205–11.[a href="/Davies%20NG,%20Klepac%20P,%20Liu%20Y,%20Prem%20K,%20Jit%20M,%20Eggo%20RM.%20Age-dependent%20effects%20in%20the%20transmission%20and%20control%20of%20Covid-1920epidemics.%20Nature%20Medicine.%202020;26:1205–11.%20doi:10.1038/s41591-020-0962-9." ">"ROSSREF] doi:10.1038/s41591-020-0962-9.

    2. Gémes K, Talbäck M, Modig K, Ahlbom A, Berglund A, Feychting M, et al. Burden and prevalence of prognostic factors for Severe COVID-19 in Sweden. European Journal of Epidemiology. 2020;35:401–9.[CROSSREF] doi:10.1007/s10654-020-00646-z.

    3. Gostin LO. The Coronavirus Pandemic 1 Year On—What Went Wrong? JAMA. 2021;325:1132.[CROSSREF] doi:10.1001/jama.2021.3207.

    4. Krouse HJ. COVID-19 and the Widening Gap in HEALTH Inequity. Otolaryngology–Head and Neck Surgery. 2020;163:65–6.[CROSSREF] doi:10.1177/0194599820926463.

    5. Wyper GM, Assunção R, Cuschieri S, Devleesschauwer B, Fletcher E, Haagsma JA, et al. Population vulnerability to COVID-19 in Europe: A burden of disease analysis. Archives of Public Health. 2020;78.[CROSSERF] doi:10.1186/s13690-020- 00433-y.

    6. Friedman J, Liu P, Troeger CE, Carter A, Reiner RC, Barber RM, et al. Predictive performance of international COVID-19 mortality forecasting models. Nature Communications. 2021;12.[CROSSREF] doi:10.1038/s41467-021-22457-w.

    7. Reiner RC, Barber RM, Collins JK, Zheng P, Adolph C, Albright J, et al. Modeling COVID-19 scenarios for the United States. Nature Medicine. 2020;27:94–105.[CROSSREF] doi:10.1038/s41591-020-1132-9.

    8. Kandel N, Chungong S, Omaar A, Xing J. Health security capacities in the context of Covid-19 outbreak: An analysis of international health Regulations annual report data from 182 countries. The Lancet. 2020;395:1047–53.[CROSSREF] doi:10.1016/s0140-6736(20)30553-5.

    9. Neogi SB, Preetha GS. Assessing health systems’ responsiveness in tackling COVID-19 pandemic. Indian Journal of Public Health. 2020;64:211.[CROSSREF] doi:10.4103/ijph.ijph_471_20.

    10. Estimation of excess mortality due to COVID-19 [Internet]. Institute for Health Metrics and Evaluation. [Internet]. Available on: http://www.healthdata.org/node/8660 [Accessed on 27 July 2021].

    11. Global COVID-19 Forecast Comparison [Internet]. GitHub. 2021. Available on: https://github.com/pyliu47/covidcompare [Accessed on 27 July 2021].

    12. Li ML, Bouardi HT, Lami OS, Trikalinos TA, Trichakis NK, Bertsimas D. Forecasting covid-19 and analyzing the effect of government interventions. 2020. doi:10.1101/2020.06.23.20138693.

    13. Forecasting the Healthcare Burden of COVID-19 in LMICs [Internet]. Imperial College London., & MRC Centre for Global Infectious Disease Analysis. 2020. Available on: https://mrc-ide.github.io/global-lmic-reports/ [Accessed on 27 July 2021].

    14. COVID-19 projections using machine learning [Internet]. Gu, Y. 2020. Available on: https://covid19-projections.com/ [Accessed on 27 July 2021].

    15. COVID-19 Confirmed and Forecasted Case Data [Internet]. Los Alamos national Laboratory COVID-19 Team. Available on: https://covid-19.bsvgateway.org [Accessed on 27 July 2021].

    16. Fast and Accurate Forecasting of COVID-19 Deaths [Internet]. Srivastava, A., Xu, T. 2020. Available on: http://arxiv.org/abs/2007.05180 [Accessed on 27 July 2021].

    17. COVID-19 National Info page [Internet]. Ministry of Health, Republic of Serbia. 2020. Available on: https://covid19.data.gov.rs [Accessed on 27 July 2021].

    18. COVID-19 Statistics in Serbia [Internet]. Ministry of Health, Republic of Serbia. 2020. Available on: https://covid19.rs/homepage-english [Accessed on 27 July 2021].

    19. Statistical release Number 203 – Year LXXI, 26 July 2021 live births and deaths, January-June 2021. SORS – Statistical Office of Serbia. 2021.Available on: https://publikacije.stat.gov.rs/G2021/PdfE/G20211203.pdf [Accessed on 27 July 2021].

    20. Statistical release Number 017 – Year LXXI, 25 January 2021 live births and deaths, January-December 2020. SORS – Statistical Office of Serbia. 2021. Available on: https://publikacije.stat.gov.rs/G2021/PdfE/G20211017.pdf [Accessed on 27 July 2021].

    21. Tashman LJ. Out-of-sample tests of forecasting accuracy: An analysis and review. International Journal of Forecasting 2000;16:437–50. doi:10.1016/ s0169-2070(00)00065-0.

    22. Viner RM, Russell SJ, Croker H, Packer J, Ward J, Stansfield C, et al. School closure and management practices during coronavirus outbreaks including covid-19: a rapid systematic review. The Lancet Child & Adolescent Health 2020;4:397–404.[CROSSREF] doi:10.1016/s2352-4642(20)30095-x.

    23. Atkeson, A. What Will Be the Economic Impact of Covid-19 in the Us? Rough Estimates of Disease Scenarios. 2020. U: Nber Working Paper Series (NBER Working Papers, Issue 26867). National Bureau of Economic Research, Inc.[HTTP]


REFERENCES

1. Davies NG, Klepac P, Liu Y, Prem K, Jit M, Eggo RM. Age-dependent effects in the transmission and control of Covid-19 epidemics. Nature Medicine. 2020;26:1205–11.[a href="/Davies%20NG,%20Klepac%20P,%20Liu%20Y,%20Prem%20K,%20Jit%20M,%20Eggo%20RM.%20Age-dependent%20effects%20in%20the%20transmission%20and%20control%20of%20Covid-1920epidemics.%20Nature%20Medicine.%202020;26:1205–11.%20doi:10.1038/s41591-020-0962-9." ">"ROSSREF] doi:10.1038/s41591-020-0962-9.

2. Gémes K, Talbäck M, Modig K, Ahlbom A, Berglund A, Feychting M, et al. Burden and prevalence of prognostic factors for Severe COVID-19 in Sweden. European Journal of Epidemiology. 2020;35:401–9.[CROSSREF] doi:10.1007/s10654-020-00646-z.

3. Gostin LO. The Coronavirus Pandemic 1 Year On—What Went Wrong? JAMA. 2021;325:1132.[CROSSREF] doi:10.1001/jama.2021.3207.

4. Krouse HJ. COVID-19 and the Widening Gap in HEALTH Inequity. Otolaryngology–Head and Neck Surgery. 2020;163:65–6.[CROSSREF] doi:10.1177/0194599820926463.

5. Wyper GM, Assunção R, Cuschieri S, Devleesschauwer B, Fletcher E, Haagsma JA, et al. Population vulnerability to COVID-19 in Europe: A burden of disease analysis. Archives of Public Health. 2020;78.[CROSSERF] doi:10.1186/s13690-020- 00433-y.

6. Friedman J, Liu P, Troeger CE, Carter A, Reiner RC, Barber RM, et al. Predictive performance of international COVID-19 mortality forecasting models. Nature Communications. 2021;12.[CROSSREF] doi:10.1038/s41467-021-22457-w.

7. Reiner RC, Barber RM, Collins JK, Zheng P, Adolph C, Albright J, et al. Modeling COVID-19 scenarios for the United States. Nature Medicine. 2020;27:94–105.[CROSSREF] doi:10.1038/s41591-020-1132-9.

8. Kandel N, Chungong S, Omaar A, Xing J. Health security capacities in the context of Covid-19 outbreak: An analysis of international health Regulations annual report data from 182 countries. The Lancet. 2020;395:1047–53.[CROSSREF] doi:10.1016/s0140-6736(20)30553-5.

9. Neogi SB, Preetha GS. Assessing health systems’ responsiveness in tackling COVID-19 pandemic. Indian Journal of Public Health. 2020;64:211.[CROSSREF] doi:10.4103/ijph.ijph_471_20.

10. Estimation of excess mortality due to COVID-19 [Internet]. Institute for Health Metrics and Evaluation. [Internet]. Available on: http://www.healthdata.org/node/8660 [Accessed on 27 July 2021].

11. Global COVID-19 Forecast Comparison [Internet]. GitHub. 2021. Available on: https://github.com/pyliu47/covidcompare [Accessed on 27 July 2021].

12. Li ML, Bouardi HT, Lami OS, Trikalinos TA, Trichakis NK, Bertsimas D. Forecasting covid-19 and analyzing the effect of government interventions. 2020. doi:10.1101/2020.06.23.20138693.

13. Forecasting the Healthcare Burden of COVID-19 in LMICs [Internet]. Imperial College London., & MRC Centre for Global Infectious Disease Analysis. 2020. Available on: https://mrc-ide.github.io/global-lmic-reports/ [Accessed on 27 July 2021].

14. COVID-19 projections using machine learning [Internet]. Gu, Y. 2020. Available on: https://covid19-projections.com/ [Accessed on 27 July 2021].

15. COVID-19 Confirmed and Forecasted Case Data [Internet]. Los Alamos national Laboratory COVID-19 Team. Available on: https://covid-19.bsvgateway.org [Accessed on 27 July 2021].

16. Fast and Accurate Forecasting of COVID-19 Deaths [Internet]. Srivastava, A., Xu, T. 2020. Available on: http://arxiv.org/abs/2007.05180 [Accessed on 27 July 2021].

17. COVID-19 National Info page [Internet]. Ministry of Health, Republic of Serbia. 2020. Available on: https://covid19.data.gov.rs [Accessed on 27 July 2021].

18. COVID-19 Statistics in Serbia [Internet]. Ministry of Health, Republic of Serbia. 2020. Available on: https://covid19.rs/homepage-english [Accessed on 27 July 2021].

19. Statistical release Number 203 – Year LXXI, 26 July 2021 live births and deaths, January-June 2021. SORS – Statistical Office of Serbia. 2021.Available on: https://publikacije.stat.gov.rs/G2021/PdfE/G20211203.pdf [Accessed on 27 July 2021].

20. Statistical release Number 017 – Year LXXI, 25 January 2021 live births and deaths, January-December 2020. SORS – Statistical Office of Serbia. 2021. Available on: https://publikacije.stat.gov.rs/G2021/PdfE/G20211017.pdf [Accessed on 27 July 2021].

21. Tashman LJ. Out-of-sample tests of forecasting accuracy: An analysis and review. International Journal of Forecasting 2000;16:437–50. doi:10.1016/ s0169-2070(00)00065-0.

22. Viner RM, Russell SJ, Croker H, Packer J, Ward J, Stansfield C, et al. School closure and management practices during coronavirus outbreaks including covid-19: a rapid systematic review. The Lancet Child & Adolescent Health 2020;4:397–404.[CROSSREF] doi:10.1016/s2352-4642(20)30095-x.

23. Atkeson, A. What Will Be the Economic Impact of Covid-19 in the Us? Rough Estimates of Disease Scenarios. 2020. U: Nber Working Paper Series (NBER Working Papers, Issue 26867). National Bureau of Economic Research, Inc.[HTTP]

1. Davies NG, Klepac P, Liu Y, Prem K, Jit M, Eggo RM. Age-dependent effects in the transmission and control of Covid-19 epidemics. Nature Medicine. 2020;26:1205–11.[a href="/Davies%20NG,%20Klepac%20P,%20Liu%20Y,%20Prem%20K,%20Jit%20M,%20Eggo%20RM.%20Age-dependent%20effects%20in%20the%20transmission%20and%20control%20of%20Covid-1920epidemics.%20Nature%20Medicine.%202020;26:1205–11.%20doi:10.1038/s41591-020-0962-9." ">"ROSSREF] doi:10.1038/s41591-020-0962-9.

2. Gémes K, Talbäck M, Modig K, Ahlbom A, Berglund A, Feychting M, et al. Burden and prevalence of prognostic factors for Severe COVID-19 in Sweden. European Journal of Epidemiology. 2020;35:401–9.[CROSSREF] doi:10.1007/s10654-020-00646-z.

3. Gostin LO. The Coronavirus Pandemic 1 Year On—What Went Wrong? JAMA. 2021;325:1132.[CROSSREF] doi:10.1001/jama.2021.3207.

4. Krouse HJ. COVID-19 and the Widening Gap in HEALTH Inequity. Otolaryngology–Head and Neck Surgery. 2020;163:65–6.[CROSSREF] doi:10.1177/0194599820926463.

5. Wyper GM, Assunção R, Cuschieri S, Devleesschauwer B, Fletcher E, Haagsma JA, et al. Population vulnerability to COVID-19 in Europe: A burden of disease analysis. Archives of Public Health. 2020;78.[CROSSERF] doi:10.1186/s13690-020- 00433-y.

6. Friedman J, Liu P, Troeger CE, Carter A, Reiner RC, Barber RM, et al. Predictive performance of international COVID-19 mortality forecasting models. Nature Communications. 2021;12.[CROSSREF] doi:10.1038/s41467-021-22457-w.

7. Reiner RC, Barber RM, Collins JK, Zheng P, Adolph C, Albright J, et al. Modeling COVID-19 scenarios for the United States. Nature Medicine. 2020;27:94–105.[CROSSREF] doi:10.1038/s41591-020-1132-9.

8. Kandel N, Chungong S, Omaar A, Xing J. Health security capacities in the context of Covid-19 outbreak: An analysis of international health Regulations annual report data from 182 countries. The Lancet. 2020;395:1047–53.[CROSSREF] doi:10.1016/s0140-6736(20)30553-5.

9. Neogi SB, Preetha GS. Assessing health systems’ responsiveness in tackling COVID-19 pandemic. Indian Journal of Public Health. 2020;64:211.[CROSSREF] doi:10.4103/ijph.ijph_471_20.

10. Estimation of excess mortality due to COVID-19 [Internet]. Institute for Health Metrics and Evaluation. [Internet]. Available on: http://www.healthdata.org/node/8660 [Accessed on 27 July 2021].

11. Global COVID-19 Forecast Comparison [Internet]. GitHub. 2021. Available on: https://github.com/pyliu47/covidcompare [Accessed on 27 July 2021].

12. Li ML, Bouardi HT, Lami OS, Trikalinos TA, Trichakis NK, Bertsimas D. Forecasting covid-19 and analyzing the effect of government interventions. 2020. doi:10.1101/2020.06.23.20138693.

13. Forecasting the Healthcare Burden of COVID-19 in LMICs [Internet]. Imperial College London., & MRC Centre for Global Infectious Disease Analysis. 2020. Available on: https://mrc-ide.github.io/global-lmic-reports/ [Accessed on 27 July 2021].

14. COVID-19 projections using machine learning [Internet]. Gu, Y. 2020. Available on: https://covid19-projections.com/ [Accessed on 27 July 2021].

15. COVID-19 Confirmed and Forecasted Case Data [Internet]. Los Alamos national Laboratory COVID-19 Team. Available on: https://covid-19.bsvgateway.org [Accessed on 27 July 2021].

16. Fast and Accurate Forecasting of COVID-19 Deaths [Internet]. Srivastava, A., Xu, T. 2020. Available on: http://arxiv.org/abs/2007.05180 [Accessed on 27 July 2021].

17. COVID-19 National Info page [Internet]. Ministry of Health, Republic of Serbia. 2020. Available on: https://covid19.data.gov.rs [Accessed on 27 July 2021].

18. COVID-19 Statistics in Serbia [Internet]. Ministry of Health, Republic of Serbia. 2020. Available on: https://covid19.rs/homepage-english [Accessed on 27 July 2021].

19. Statistical release Number 203 – Year LXXI, 26 July 2021 live births and deaths, January-June 2021. SORS – Statistical Office of Serbia. 2021.Available on: https://publikacije.stat.gov.rs/G2021/PdfE/G20211203.pdf [Accessed on 27 July 2021].

20. Statistical release Number 017 – Year LXXI, 25 January 2021 live births and deaths, January-December 2020. SORS – Statistical Office of Serbia. 2021. Available on: https://publikacije.stat.gov.rs/G2021/PdfE/G20211017.pdf [Accessed on 27 July 2021].

21. Tashman LJ. Out-of-sample tests of forecasting accuracy: An analysis and review. International Journal of Forecasting 2000;16:437–50. doi:10.1016/ s0169-2070(00)00065-0.

22. Viner RM, Russell SJ, Croker H, Packer J, Ward J, Stansfield C, et al. School closure and management practices during coronavirus outbreaks including covid-19: a rapid systematic review. The Lancet Child & Adolescent Health 2020;4:397–404.[CROSSREF] doi:10.1016/s2352-4642(20)30095-x.

23. Atkeson, A. What Will Be the Economic Impact of Covid-19 in the Us? Rough Estimates of Disease Scenarios. 2020. U: Nber Working Paper Series (NBER Working Papers, Issue 26867). National Bureau of Economic Research, Inc.[HTTP]


© All rights reserved. Medical Chamber of Serbia.

To top