. 2020 Sep 12;6(4):468–479. doi: 10.1007/s40726-020-00161-5

Table 1.

Models developed to study virus transmission in the environment

Study	Novelty + study region	Methodology + major results	Conclusion
Yap et al. 2020 [85]	Lab-scale, an analytical model, based on Arrhenius equation and rate law.	The inactivation of virus follows the first-order kinetics C = C_oe^-kT. The temperature-dependent denaturation of viral proteins has been explained through Arrhenius equation: ln(k) = −Ea/RT + ln(A). Further, combining the two equations resulted in determining the time of reduction of pathogen by n-folds as given below $t = \frac{- 1}{A} e^{\frac{Ea}{RT} ln (10^{- n})}$	The study predicted the lifetime of the virus on a surface as that time, which achieves a 6-log reduction in concentration based on the recommendation of the US FDA. The study provides thermal sterilization guidelines to the healthcare workers exposed at the forefronts of the virus outbreak.
Sajadi et al. 2020 [86]	Temperature, humidity and latitude impact on COVID-19 spread and identification of any seasonality trend across the globe.	Regions with significant cases of community transmission are located between the 30°–50° N belt. The identified belt has similar meteorological conditions, including temperature (5–11 °C) and low absolute humidity ranging between 4 and 7 g/m³. Study is assisted by ERA-5 Reanalysis product.	The study suggested strong seasonal influence on 2019-nCoV spread, with the rate of transmission slowing during the summers.
Shi et al. 2020 [87]	Impact of temperature and relative humidity on virus transmission rate in China.	No association between the relative humidity and the COVID-19 incidence was established. However, with increasing temperature, the transmission rate declined.	Models can be used in order to understand and quantify the rate of transmission of COVID-19 incidence with a change in temperature.
Amin et al. 2020 [88]	Role of climatic differences on COVID-19 spread in Iraqi Kurdistan region.	Data analysis of the COVID-19 infection with temperature and humidity suggested that high climatic spatial differences may facilitate the infection spread.	Regions with high seasonal and spatial climatic differences carry high susceptibility in terms of disease spread.
Bherwani et al. 2020 [89]	The impact of COVID-19 incidence was analysed using SEIR model, and the impact of temperature and humidity was statistically analysed using Response Surface Methodology in India	The impact of government-imposed lockdown was considered in the SEIR (compartmental) model. Further, ANOVA and higher-order polynomial functions of RSM techniques provide much better estimates.	The model predicts 20-day increase in crisis management for the inability to implement a strict lockdown. It also showed that hot weather might significantly reduce the spread of the virus.
Yao et al. 2020 [90]	Correlation between COVID-19 related death rate and PM concentration in China	Multiple linear regression model was used to establish the said correlation by adjusting parameters such as temperature, GDP, relative humidity and hospital beds per capita.	The study speculated that the effect of air pollution is only limited to making symptoms severe from moderate.
Toppi et al. 2020 [91]	Associating high atmospheric pollution with increased duration of 2019-nCoV suspension in the air due to surface adsorption in Italy.	The high peak of PM₁₀ and PM_2.5 of 120–130 μg m⁻³ and 100 μg m⁻³, respectively, during the infection peak, suggests a possible role of atmospheric adsorption of the virus in regions with widespread community outbreaks.	To counter the future wave of respiratory infections, more emphasis should be given to decrease the load of atmospheric pollutants.