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      On the reliability of predictions on Covid-19 dynamics: A systematic and critical review of modelling techniques

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          Abstract

          Since the emergence of the novel 2019 coronavirus pandemic in December 2019 (COVID-19), numerous modellers have used diverse techniques to assess the dynamics of transmission of the disease, predict its future course and determine the impact of different control measures. In this study, we conducted a global systematic literature review to summarize trends in the modelling techniques used for Covid-19 from January 1st , 2020 to October 30th , 2020. We further examined the reliability and correctness of predictions by comparing predicted and observed values for cumulative cases and deaths. From an initial 4311 peer-reviewed articles and preprints found with our defined keywords, 242 were fully analysed. Most studies were done on Asian (46.52%) and European (27.39%) countries. Most of them used compartmental models (namely SIR and SEIR) (46.1%) and statistical models (growth models and time series) (31.8%) while few used artificial intelligence (6.7%), Bayesian approach (4.7%), Network models (2.3%) and Agent-based models (1.3%). For the number of cumulative cases, the ratio of the predicted over the observed values and the ratio of the amplitude of confidence interval (CI) or credibility interval (CrI) of predictions and the central value were on average larger than 1 indicating cases of inaccurate and imprecise predictions, and large variation across predictions. There was no clear difference among models used for these two ratios. In 75% of predictions that provided CI or CrI, observed values fall within the 95% CI or CrI of the cumulative cases predicted. Only 3.7% of the studies predicted the cumulative number of deaths. For 70% of the predictions, the ratio of predicted over observed cumulative deaths was less or close to 1. Also, the Bayesian model made predictions closer to reality than classical statistical models, although these differences are only suggestive due to the small number of predictions within our dataset (9 in total). In addition, we found a significant negative correlation (rho = - 0.56, p = 0.021) between this ratio and the length (in days) of the period covered by the modelling, suggesting that the longer the period covered by the model the likely more accurate the estimates tend to be. Our findings suggest that while predictions made by the different models are useful to understand the pandemic course and guide policy-making, some were relatively accurate and precise while other not.

          Highlights

          • 46% of studies used compartmental models, 32% statistical models, and 1% individual-based models.

          • Predicted cumulative cases were larger than values observed in reality for 1/3 of the predictions.

          • Observed values were within the 95% CI or CrI of predicted number of cumulative cases in 75% of predictions.

          • The wider the time covered by the data, the better the accuracy of predictions for the number of cumulative deaths.

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          Most cited references52

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          Presumed Asymptomatic Carrier Transmission of COVID-19

          This study describes possible transmission of novel coronavirus disease 2019 (COVID-19) from an asymptomatic Wuhan resident to 5 family members in Anyang, a Chinese city in the neighboring province of Hubei.
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            Is Open Access

            Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020

            On 5 February 2020, in Yokohama, Japan, a cruise ship hosting 3,711 people underwent a 2-week quarantine after a former passenger was found with COVID-19 post-disembarking. As at 20 February, 634 persons on board tested positive for the causative virus. We conducted statistical modelling to derive the delay-adjusted asymptomatic proportion of infections, along with the infections’ timeline. The estimated asymptomatic proportion was 17.9% (95% credible interval (CrI): 15.5–20.2%). Most infections occurred before the quarantine start.
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              The COVID‐19 epidemic

              The current outbreak of the novel coronavirus SARS‐CoV‐2 (coronavirus disease 2019; previously 2019‐nCoV), epi‐centred in Hubei Province of the People’s Republic of China, has spread to many other countries. On 30. January 2020, the WHO Emergency Committee declared a global health emergency based on growing case notification rates at Chinese and international locations. The case detection rate is changing daily and can be tracked in almost real time on the website provided by Johns Hopkins University 1 and other forums. As of midst of February 2020, China bears the large burden of morbidity and mortality, whereas the incidence in other Asian countries, in Europe and North America remains low so far. Coronaviruses are enveloped, positive single‐stranded large RNA viruses that infect humans, but also a wide range of animals. Coronaviruses were first described in 1966 by Tyrell and Bynoe, who cultivated the viruses from patients with common colds 2. Based on their morphology as spherical virions with a core shell and surface projections resembling a solar corona, they were termed coronaviruses (Latin: corona = crown). Four subfamilies, namely alpha‐, beta‐, gamma‐ and delta‐coronaviruses exist. While alpha‐ and beta‐coronaviruses apparently originate from mammals, in particular from bats, gamma‐ and delta‐viruses originate from pigs and birds. The genome size varies between 26 kb and 32 kb. Among the seven subtypes of coronaviruses that can infect humans, the beta‐coronaviruses may cause severe disease and fatalities, whereas alpha‐coronaviruses cause asymptomatic or mildly symptomatic infections. SARS‐CoV‐2 belongs to the B lineage of the beta‐coronaviruses and is closely related to the SARS‐CoV virus 3, 4. The major four structural genes encode the nucleocapsid protein (N), the spike protein (S), a small membrane protein (SM) and the membrane glycoprotein (M) with an additional membrane glycoprotein (HE) occurring in the HCoV‐OC43 and HKU1 beta‐coronaviruses 5. SARS‐CoV‐2 is 96% identical at the whole‐genome level to a bat coronavirus 4. SARS‐CoV‐2 apparently succeeded in making its transition from animals to humans on the Huanan seafood market in Wuhan, China. However, endeavours to identify potential intermediate hosts seem to have been neglected in Wuhan and the exact route of transmission urgently needs to be clarified. The initial clinical sign of the SARS‐CoV‐2‐related disease COVID‐19 which allowed case detection was pneumonia. More recent reports also describe gastrointestinal symptoms and asymptomatic infections, especially among young children 6. Observations so far suggest a mean incubation period of five days 7 and a median incubation period of 3 days (range: 0–24 days) 8. The proportion of individuals infected by SARS‐CoV‐2 who remain asymptomatic throughout the course of infection has not yet been definitely assessed. In symptomatic patients, the clinical manifestations of the disease usually start after less than a week, consisting of fever, cough, nasal congestion, fatigue and other signs of upper respiratory tract infections. The infection can progress to severe disease with dyspnoea and severe chest symptoms corresponding to pneumonia in approximately 75% of patients, as seen by computed tomography on admission 8. Pneumonia mostly occurs in the second or third week of a symptomatic infection. Prominent signs of viral pneumonia include decreased oxygen saturation, blood gas deviations, changes visible through chest X‐rays and other imaging techniques, with ground glass abnormalities, patchy consolidation, alveolar exudates and interlobular involvement, eventually indicating deterioration. Lymphopenia appears to be common, and inflammatory markers (C‐reactive protein and proinflammatory cytokines) are elevated. Recent investigations of 425 confirmed cases demonstrate that the current epidemic may double in the number of affected individuals every seven days and that each patient spreads infection to 2.2 other individuals on average (R0) 6. Estimates from the SARS‐CoV outbreak in 2003 reported an R0 of 3 9. A recent data‐driven analysis from the early phase of the outbreak estimates a mean R0 range from 2.2 to 3.58 10. Dense communities are at particular risk and the most vulnerable region certainly is Africa, due to dense traffic between China and Africa. Very few African countries have sufficient and appropriate diagnostic capacities and obvious challenges exist to handle such outbreaks. Indeed, the virus might soon affect Africa. WHO has identified 13 top‐priority countries (Algeria, Angola, Cote d’Ivoire, the Democratic Republic of the Congo, Ethiopia, Ghana, Kenya, Mauritius, Nigeria, South Africa, Tanzania, Uganda, Zambia) which either maintain direct links to China or a high volume of travel to China. Recent studies indicate that patients ≥60 years of age are at higher risk than children who might be less likely to become infected or, if so, may show milder symptoms or even asymptomatic infection 7. As of 13. February 2020, the case fatality rate of COVID‐19 infections has been approximately 2.2% (1370/60363; 13. February 2020, 06:53 PM CET); it was 9.6% (774/8096) in the SARS‐CoV epidemic 11 and 34.4% (858/2494) in the MERS‐CoV outbreak since 2012 12. Like other viruses, SARS‐CoV‐2 infects lung alveolar epithelial cells using receptor‐mediated endocytosis via the angiotensin‐converting enzyme II (ACE2) as an entry receptor 4. Artificial intelligence predicts that drugs associated with AP2‐associated protein kinase 1 (AAK1) disrupting these proteins may inhibit viral entry into the target cells 13. Baricitinib, used in the treatment of rheumatoid arthritis, is an AAK1 and Janus kinase inhibitor and suggested for controlling viral replication 13. Moreover, one in vitro and a clinical study indicate that remdesivir, an adenosine analogue that acts as a viral protein inhibitor, has improved the condition in one patient 14, 15. Chloroquine, by increasing the endosomal pH required for virus‐cell fusion, has the potential of blocking viral infection 15 and was shown to affect activation of p38 mitogen‐activated protein kinase (MAPK), which is involved in replication of HCoV‐229E 16. A combination of the antiretroviral drugs lopinavir and ritonavir significantly improved the clinical condition of SARS‐CoV patients 17 and might be an option in COVID‐19 infections. Further possibilities include leronlimab, a humanised monoclonal antibody (CCR5 antagonist), and galidesivir, a nucleoside RNA polymerase inhibitor, both of which have shown survival benefits in several deadly virus infections and are being considered as potential treatment candidates 18. Repurposing these available drugs for immediate use in treatment in SARS‐CoV‐2 infections could improve the currently available clinical management. Clinical trials presently registered at ClinicalTrials.gov focus on the efficacy of remdesivir, immunoglobulins, arbidol hydrochloride combined with interferon atomisation, ASC09F+Oseltamivir, ritonavir plus oseltamivir, lopinavir plus ritonavir, mesenchymal stem cell treatment, darunavir plus cobicistat, hydroxychloroquine, methylprednisolone and washed microbiota transplantation 19. Given the fragile health systems in most sub‐Saharan African countries, new and re‐emerging disease outbreaks such as the current COVID‐19 epidemic can potentially paralyse health systems at the expense of primary healthcare requirements. The impact of the Ebola epidemic on the economy and healthcare structures is still felt five years later in those countries which were affected. Effective outbreak responses and preparedness during emergencies of such magnitude are challenging across African and other lower‐middle‐income countries. Such situations can partly only be mitigated by supporting existing regional and sub‐Saharan African health structures.
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                Author and article information

                Journal
                Infect Dis Model
                Infect Dis Model
                Infectious Disease Modelling
                KeAi Publishing
                2468-2152
                2468-0427
                12 January 2021
                12 January 2021
                Affiliations
                [1]Laboratoire de Biomathématiques et d’Estimations Forestières, Université d’Abomey-Calavi, 04 BP 1525, Cotonou, Benin
                Author notes
                []Corresponding author.
                Article
                S2468-0427(20)30112-3
                10.1016/j.idm.2020.12.008
                7802527
                33458453
                b87f9b99-c0a3-4d01-a11f-5a00a2f9e001
                .

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 16 September 2020
                : 29 December 2020
                : 29 December 2020
                Categories
                Article

                predictions,accuracy,correctness,sars-cov-2,pandemic,ratio
                predictions, accuracy, correctness, sars-cov-2, pandemic, ratio

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