key: cord-0258147-yyy7ltuy authors: Suphanchaimat, Rapeepong; Teekasap, Pard; Nittayasoot, Natthaprang; Phaiyarom, Mathudara; Cetthakrikul, Nisachol title: Forecasted trends of the new COVID-19 epidemic due to the Omicron variant in Thailand, 2022 date: 2022-01-24 journal: bioRxiv DOI: 10.1101/2022.01.24.477479 sha: dd2567d47d2cbcc93bf90978ae30804759473352 doc_id: 258147 cord_uid: yyy7ltuy Background The introduction of the Omicron variant is of significant concern to the Thai Government due to the possibility of a new wave of the COVID-19 epidemic, which may cause a huge strain to the country’s health system. This study aims to forecast the trends of COVID-19 cases and deaths given the advent of the Omicron variant in Thailand. Methods We used a compartmental susceptible-exposed-infectious-recovered model in combination with a system dynamics model. We developed four scenarios according to differing values of the production number (R) and varying vaccination rates. Results The findings indicated that in the most pessimistic scenario (R = 7.5 and base vaccination rate), the number of incident cases reached a peak of 49,523 (95% CI: 20,599 to 99,362) by day 73 and the peak daily deaths enlarged to 270 by day 50 (95% CI: 124 to 520). The predicted cumulative cases and deaths at the end of the wave (day 120) were approximately 3.7 million and 22,000 respectively. In the most optimistic assumption (with R = 4.5 and a speedy vaccination rate [tripled the base rate]), the peak of the incident cases was about one third of the most pessimistic assumption (15,650, 95% CI: 12,688 to 17,603). The corresponding daily fatalities were 72 (95% CI: 54 to 84) and the prevalent intubated cases numbered 572 (95% CI: 429 to 675). Conclusions In the coming months, Thailand may face a new wave of the COVID-19 epidemic due to the Omicron variant. The case toll due to the Omicron wave is likely to outnumber the earlier Delta wave, but the death toll is proportionately lower. Despite the immune-escape characteristic of the Omicron variant, the vaccination campaign for the booster dose should be expedited as an effective way of preventing severe illness and death. subsided to a level below 3,000 by mid-December 2021 [16] . As the situation appeared to be 88 relieved, the Government later withdrew the lockdown policy by November 2021 but still 89 encouraged people to keep physical distancing and maintaining mask wearing in public spaces. 90 To prepare for the resuming of international flights to boost the country's touristy businesses, the 91 Government planned to implement a "Test & Go" policy in which an inbound traveler is not 92 required to undertake a 14-day stay in the quarantine center as long as he/she is fully vaccinated Omicron variant was confirmed. The situation caused concern for the Government because the 101 Omicron variant could create a serious threat to the Thai healthcare system similar to that during 102 the Delta pandemic. This point informs the objective of this study. 103 This study aims to forecast the trends of new cases as well as the death toll and use of health 104 resources for severe cases given the advent of the Omicron variant in Thailand. We hope that the 105 findings of this study will help aid policy decisions for optimal preparation of the healthcare 106 system resources, and highlight the importance of measures (including vaccines and nonpharmaceutical interventions [NPI] ) which may help mitigate the outbreak magnitude. writing. We therefore adopted the parameters specific to the Delta variant instead.  Model framework 119 We employed a compartmental susceptible-exposed-infectious-recovered (SEIR) model and the 120 system dynamics (SD) model to frame the analysis [18, 19] . The simplified model framework is 121 demonstrated in Fig 1. We divided the entire Thai population into four groups based on the 122 vaccination profile: (i) the unvaccinated, (ii) the one-dose, (iii) the two-dose, and (iii) the booster 123 (receiving at least three shots of vaccine). In each group, we sub-categorized the population into 124 five sub-categories according to the infection status: (i) the susceptible, (ii) the exposed, (iii) the 125 infectious before isolation, (iv) the infectious after isolation, and (v) the recovered. The speed of transfer from susceptible group to exposed group was mainly influenced by the 127 reproduction number (R) [20] . The transition from the exposed group to the infectious group 128 depended on the incubation period. We adapted the traditional SEIR model by splitting the 129 infectious group into before isolation and after isolation. The reason behind this is that once admitted to a hospital, an infected person would be isolated by the hospital protocol (suppose no 131 nosocomial infection). The length of stay (LOS) in a hospital influenced the speed of recovery. Among the admitted patients, the prevalence of intubated cases attracted the attention of policy 133 makers the most. This is because the volume of intubated cases represent the reserve capacity of 134 intensive care, while asymptomatic or mild cases are allowed to be isolated at home or in the 135 community according to the current MOPH protocol [21] . We further assumed that some of the 136 intubated cases later died and no deaths occurred without intubation. An unvaccinated 137 susceptible person encountered two paths, either becoming exposed to the disease or remaining 138 as a susceptible person and receiving the first vaccine shot, which depended on the vaccination 139 rate in the entire population. The same concept also applied for the one-dose, the two-dose, and 140 the booster groups. Second, in most pandemics, the exact number of initial infectees could be hardly identified. We only on the probability from being susceptible to being exposed but also altered the severity profile of the infectious compartment (reducing the probability of becoming severe cases or 173 deaths). Since no officially published report of the VE against the Omicron infection in Thailand 174 had come out yet, we used the VE of the viral-vector vaccines in the UK instead (also the same 175 vaccine type widely administered in Thailand) [27] . 176 Sixth, we used the vaccination rate by late 2021 as a base vaccination rate in the population and 177 assumed that this remained unchanged throughout the study course. We touched upon the 178 vaccination rate again in the later section, "Model scenarios and interested outcomes". The Helsinki. 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