key: cord-0286028-mz2l1at6 authors: Bi, K.; Herrera-Diestra, J. L.; Bai, Y.; Du, Z.; Wang, L.; Gibson, G.; Johnson-Leon, M.; Fox, S.; Meyers, L. A. title: The risk of SARS-CoV-2 Omicron variant emergence in low and middle-income countries (LMICs) date: 2022-01-14 journal: nan DOI: 10.1101/2022.01.14.22268821 sha: 481e550952080ceb4da9c6c1659d40acb71e2d66 doc_id: 286028 cord_uid: mz2l1at6 We estimated the probability of undetected emergence of the SARS-CoV-2 Omicron variant in 25 low and middle-income countries (LMICs) prior to December 5, 2021. In nine countries, the risk exceeds 50%; in Turkey, Pakistan and the Philippines, it exceeds 99%. Risks are generally lower in the Americas than Europe or Asia. The B.1.1.529 SARS-COV-2 variant was first detected and reported in South Africa on November 26 th , 2021 [1] . By December 5 th , 2021, more than 40 countries reported Omicron variant cases. Most of these countries are developed and high-income countries, including the United Kingdom, the United States, and Netherlands [2] . However, low and middle income countries (LMICs) may be less likely to detect a new variant [3] and more vulnerable to catastrophic public health outcomes than high-income countries, because of lower capacity for COVID-19 testing, vaccination, and medical treatment [4, 5] . Only a few LMICs have direct flights from the countries in Southern Africa where Omicron was initially detected [6] and many enacted border policies to reduce Omicron importation risks from these countries [7] . However, LMICs were at risk for Omicron importations from large international destinations outside of Southern Africa in which Omicron emerged in late 2021. We analyzed the risks of the Omicron variant importation in 25 LMICs in which Omicron was not reported as of December 5, 2021: Bangladesh, Nepal, Philippines, Colombia, Egypt, Pakistan, Paraguay, Turkey, Serbia, Bolivia, Argentina, Uruguay, Bhutan, Indonesia, Albania, Jordan, Panama, Dominican Republic, Ecuador, Peru, Jamaica, Honduras, Guatemala, Costa Rica, and El Salvador. We first estimated the daily travel volume to each country from 13 large countries in which Omicron had already been detected, based on data from Facebook Data for Good ( Figure A ) [8] . We estimated the prevalence of Omicron in each of the 13 Omicron detected countries (ODCs) assuming that only 2.5% of early cases were identified and reported [2, 9] , and then estimated the probability of travel-based introductions into each LMIC by December 5, 2021 (see Supporting Information). The European LMICs (Serbia and Turkey), which are highly connected to Western European countries that reported Omicron cases by November 2021, have the highest estimated risks, followed by the Asian LMICs (Pakistan, Bangladesh, and Nepal), with high inflows of travelers from South Africa (via connecting flights), the UK, and India. LMICs in the Americas (Colombia, Dominican Republic, and Paraguay) are primarily at risk for importations from the US and Brazil. We estimate that 6 of the 25 studied LMICs had over a 50% chance of having received at least one travel-based Omicron importation from ODCs by December 5, 2021 ( Figure B ). To assess the risk of Omicron transmission following importation, we estimate the immunity-based effective reproduction number ( ) in each LMIC as of December 5, 2021, based on reported vaccination levels [10] , estimates of infection-acquired immunity [11] If these countries implement nonpharmaceutical interventions that reduce transmission by 80%, the probability of undetected emergence declines by 12.02% to 80.77%% across the 25 LMICs ( Figure E) . Given the high socioeconomic costs of travel restrictions and some non-pharmaceutical interventions, many of these LMICs did not take measures to prevent introductions or slow spread [14] . Our analyses suggest that SARS-CoV-2 variants like Omicron can rapidly emerge in LMICs and spread for weeks before detection. The authors were granted by Facebook Data for Good team to use the Travel Pattern data in research. The results of the research conducted are approved for sharing. The access to the Facebook Data for Good database for research purpose could be granted after the registration. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted January 14, 2022. ; [8] . B. The probability of receiving at least one Omicron importation via travelers from the 13 ODCs between November 16 and December 5, 2021. C. Estimated vaccination and prior infection rates for each LMIC, assuming a 40% infection reporting rate for all countries [15] . D. Estimated effective reproduction number for the Omicron variant in each LMIC, assuming that Omicron is twice as transmissible as the Delta variant and that vaccines have 50% lower efficacy against Omicron in comparison to Delta. E. Comparisons of the estimated risks of the undetected Omicron transmission in LMICs with and without intervention that reduces transmission by 80%. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted January 14, 2022. ; https://doi.org/10.1101/2022.01.14.22268821 doi: medRxiv preprint Update on Omicron GISAID -HCov19 Variants COVID-19 research in LMICs Limited resources of genome sequencing in developing countries: challenges and solutions Managing COVID-19 in resource-limited settings: critical care considerations Live Flight Tracker -Real-Time Flight Tracker Map Omicron-variant border bans ignore the evidence, say scientists Data for Good Tools and Data Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave A global database of COVID-19 vaccinations World Health Organization. 2021 Edge-based compartmental modelling for infectious disease spread Omicron (B. 1.1. 529): Infectivity, vaccine breakthrough, and antibody resistance Stark choices: exploring health sector costs of policy responses to COVID-19 in low-income and middle-income countries Estimating SARS-CoV-2 Infections from Deaths, Confirmed Cases, Tests, and Random Surveys This research was supported by grants from the US National Institutes of Health (grant no. R01 AI151176) and the US Centers for Disease Control and Prevention (grant no. U01 IP001136) and a donation from Love, Tito's (the philanthropic arm of Tito's Homemade Vodka, Austin, TX, USA) to the University of Texas to support the modeling of COVID-19 mitigation strategies.