Table 1.
The 33 selected papers on COVID-19 compartmental models.
| Models abbreviation | Model type | Compartments | Study area | Outcomes | References |
|---|---|---|---|---|---|
| SEIR/SLIR | Stochastic (Chinazzi & Davis, 2020; Kai & Guy-PhilippeGoldstein, 2020; Kucharski et al., 2020; Sanche et al., 2020; Tian, Liu, & Li, 2020; Wang & Liu, 2020; Wu, Leung, & Leung, 2020; Yang et al., 2020; Zhao, Stone, & Gao, 2020), deterministic (Acuna-Zegarra, Santana-Cibrian, & Velasco-Hernandez, 2020; Boldog et al., 2020; Hauser et al., 2020; Hou et al., 2020; Kuniya, 2020; Mandal, Bhatnagar, & Arinaminpathy, 2020; Ngonghala et al., 2020; Tang, Bragazzi, et al., 2020; Tang, Wang, et al., 2020) | Susceptible(S), exposed/latent(E/L), infectious(I), removed(R) | China(Tang, Bragazzi, et al., 2020; Tian et al., 2020; Wu et al., 2020), Wuhan (Chinazzi & Davis, 2020; Hou et al., 2020; Kucharski et al., 2020; Sanche et al., 2020; Tang, Wang, et al., 2020; Wang & Liu, 2020; Zhao et al., 2020), Hubei, China and northern Italy(Hauser et al., 2020), 38 countries or provinces in Asia, Europe and North America (Kai & Guy-PhilippeGoldstein, 2020), New York(Ngonghala et al., 2020), Mexico(Acuna-Zegarra et al., 2020), Zhejiang, Guangdong, Hubei, China(Yang et al., 2020), Japan(Kuniya, 2020), India (S. Mandal, Bhatnagar, & Arinaminpathy, 2020), outside China(Boldog et al., 2020) | R0(Chinazzi & Davis, 2020; Kucharski et al., 2020; Sanche et al., 2020; Tang, Bragazzi, et al., 2020; Tang, Wang, et al., 2020; Tian et al., 2020; Wang & Liu, 2020; Wu et al., 2020; Zhao et al., 2020, Zhao et al., 2020, Zhao et al., 2020) (Acuna-Zegarra et al., 2020; Boldog et al., 2020; Kuniya, 2020; S.; Mandal, Bhatnagar, & Arinaminpathy, 2020), mortality (Hauser et al., 2020), infectious period(Acuna-Zegarra et al., 2020), doubling time (Chinazzi & Davis, 2020), prediction of epidemic (Ngonghala et al., 2020; Yang et al., 2020), the impact of transmission control measures (Tian et al., 2020), the impact of travel restrictions(Chinazzi & Davis, 2020), the impact of universal masking (Kai & Guy-PhilippeGoldstein, 2020), the impact of behavioral changes (Acuna-Zegarra et al., 2020), the impact of interventions (Ngonghala et al., 2020), the epidemic risk of imported cases from outside China (Boldog et al., 2020), the impact of quarantine of Wuhan city (Hou et al., 2020) | (Acuna-Zegarra et al., 2020; Boldog et al., 2020; Chinazzi & Davis, 2020; Hauser et al., 2020; Hou et al., 2020; Kai & Guy-PhilippeGoldstein, 2020; Kucharski et al., 2020; Kuniya, 2020; S. Mandal, Bhatnagar, & Arinaminpathy, 2020; Ngonghala et al., 2020; Sanche et al., 2020; Tang, Bragazzi, et al., 2020; Tang, Wang, et al., 2020; Tian et al., 2020; Wang & Liu, 2020; Wu et al., 2020; Yang et al., 2020; Zhao et al., 2020, Zhao et al., 2020, Zhao et al., 2020) |
| SIRD | Deterministic | Susceptible (S), infected (I), recovered (R), dead (D) | China, Italy and France(Fanelli, Piazza, & 2020), Hubei(Anastassopoulou, Russo, Tsakris, & Siettos, 2020) | R0 (Anastassopoulou et al., 2020), the prediction of the COVID-19 outbreak (Fanelli & Piazza, 2020), mortality(Anastassopoulou et al., 2020; Fanelli & Piazza, 2020) | (Anastassopoulou et al., 2020; Fanelli & Piazza, 2020) |
| SEIRS | Stochastic | Susceptible(S), exposed (E), infectious(I), recovered(R), susceptible(S) | Temperate regions | R0, the transmission dynamics of SARS-CoV-2 through the post-pandemic period | Kissler, Tedijanto, Goldstein, Grad, and Lipsitch (2020) |
| SIR-X | Deterministic | Infected(I), susceptible(S), removed(R), quarantined(X) | China | R0, the impact of containment policies | Maier and Brockmann (2020) |
| SEIHARD | Deterministic | Susceptible(S), exposed(E), symptomatic infectious(I), hospitalized(H), asymptomatic infectious(A), recovered(R), deaths(D) | Washington, New York | The impact of universal masking | Eikenberry et al. (2020) |
| SEIRU | Deterministic | Susceptible(S), asymptomatic noninfectious (E), asymptomatic infectious(I), reported symptomatic infectious (R), unreported symptomatic infectious (U) | China | R0, transmission rate, the role of the exposed or latency period | Liu, Magal, Seydi, and Webb (2020a) |
| SIRU | Deterministic | Susceptible(S),asymptomatic infectious (I), reported symptomatic infectious(R), unreported symptomatic infectious (U) | Korea, Italy, France and Germany (Magal & Webb, 2020), China, Hubei, Wuhan (Liu, Magal, Seydi, & Webb, 2020c), China(Liu, Magal, Seydi, & Webb, 2020b) | The prediction of cumulative confirmed cases (Liu et al., 2020b, 2020c), the understanding of unreported cases(Liu et al., 2020c) | (Liu et al, 2020b, 2020c; Magal & Webb, 2020) |
| SEIIN | Stochastic | Susceptible(S), exposed(E), documented infected(I), undocumented infected(I),total population(N) | China | R0, latent period, infectious period, the fraction of undocumented infections and their contagiousness | (R. Li, Pei, et al., 2020) |
| SEIQR | Deterministic | Susceptible(S), exposed(E), hospitalized infected(I), quarantine(Q), recovered or removed(R) | India | Short-term prediction of COVID-19 | (M. Mandal, Bhatnagar, & Arinaminpathy, 2020) |
| SEIRQ | Stochastic | Susceptible(S), exposed (E), infectious (I), removed (R), quarantine(Q) | Guangdong | Short-term prediction of COVID-19 | Hu et al. (2020) |
| SCIRA | Stochastic | Susceptible (S), closely observed (C), infected patients (I), recovered (R, cured/dead), asymptomatic (A). | Jiangsu, Anhui | Asymptomatic infection ratio, the effects of asymptomatic and imported patients | Sun and Weng (2020) |
| SEIHR | Deterministic | Susceptible (S), exposed (E), symptomatic infectious (I), hospitalized (H), recovered or death (R) | South Korea | R0, the impact of interventions | Choi and Ki (2020) |
| SEIPAHRF | Deterministic | Susceptible (S), exposed (E), symptomatic (I), super-spreaders class (P), asymptomatic infectious (A), hospitalized (H), recovery (R), fatality(F). | Wuhan | R0(focus on the transmissibility of super-spreaders individuals) | Ndaïrou, Area, Nieto, and Torres (2020) |