| Literature DB >> 33387315 |
Sanjeev Kumar1, Ritu Singh2, Nisha Kumari3, Susmita Karmakar1, Monalisha Behera3, Arif Jamal Siddiqui4, Vishnu D Rajput5, Tatiana Minkina5, Kuldeep Bauddh1, Narendra Kumar6.
Abstract
Coronavirus disease 2019 (COVID-19) has emerged as a significant public health emergency in recent times. It is a respiratory illness caused by the novel virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which was initially reported in late December 2019. In a span of 6 months, this pandemic spread across the globe leading to high morbidity and mortality rates. Soon after the identification of the causative virus, questions concerning the impact of environmental factors on the dissemination and transmission of the virus, its persistence in environmental matrices, and infectivity potential begin to emerge. As the environmental factors could have far-reaching consequences on infection dissemination and severity, it is essential to understand the linkage between these factors and the COVID-19 outbreak. In order to improve our current understanding over this topic, the present article summarizes topical and substantial observations made regarding the influences of abiotic environmental factors such as climate, temperature, humidity, wind speed, air, and water quality, solid surfaces/interfaces, frozen food, and biotic factors like age, sex, gender, blood type, population density, behavioural characteristics, etc. on the transmission, persistence, and infectivity of this newly recognized SARS-CoV-2 virus. Further, the potential pathways of virus transmission that could pose risk to population health have been discussed, and the critical areas have been identified which merits urgent research for the assessment and management of the COVID-19 outbreak. Where possible, the knowledge gaps requiring further investigation have been highlighted.Entities:
Keywords: COVID-19; Cold chain transportation; Environmental factors; Infectivity; Persistence; SARS-CoV-2; Transmission
Mesh:
Substances:
Year: 2021 PMID: 33387315 PMCID: PMC7776306 DOI: 10.1007/s11356-020-12165-1
Source DB: PubMed Journal: Environ Sci Pollut Res Int ISSN: 0944-1344 Impact factor: 4.223
Fig. 1Major disease outbreaks in recent decades
Characteristics of SARS-CoV-2
| Particulars | Characteristics |
|---|---|
| Family | Coronaviridae |
| Sub family | Orthocoronavirinae |
| Order | Nidovirales |
| Genera | Four: αCoV, β-CoV, γ-CoV, and δ-CoV |
| RNA | Linear, single-stranded RNA genomes of positive polarity |
| Infection | |
| α-CoV and β-CoV | Infect the respiratory, gastrointestinal, and CNS of humans and mammals |
| γ-CoV and δ-CoV | Infect the birds |
| β-CoV | SARS-CoV (2002–2003) |
| MERS-CoV (2012) | |
| 2019-nCoV (2019) | |
| Nucleotide sequence similarity | 79% with SARS-CoV and 50% with MERS-CoV |
| Natural host | Bat; 96.2% genome sequence similarity to RaTG13, a bat coronavirus detected in, |
| Intermediate hosts | Pangolins; 99% genome sequence similarity with pangolin-CoV |
| Protein structure | Spike protein (S-protein) |
| Receptor | Human angiotensin converting enzyme 2 (ACE2) |
| Binding affinity | High affinity between ACE2 and SARS-CoV-2 S protein Population with higher expression of ACE2 might be more susceptible to COVID-19 |
Fig. 2Various possible transmission routes of SARS-CoV-2 in environment
Fig. 3Influence of Environmental factors on SARS-CoV-2 infection
Literature survey on COVID-19 and meteorological conditions
| Study area | Time span | Meteorological variables | Inferences | References |
|---|---|---|---|---|
| 31 provinces of China | January 23 to March 1, 2020 | Temperature, relative humidity, precipitation, and wind speed | Doubling time of COVID-19 cases was associated with temperature and relative humidity but precipitation and wind speed didn’t have any influence. | Oliveiros et al. |
| Non-tropical countries | January 20 to March 19, 2020 | Temperature and humidity | Absolute humidity (AH) is much important weather variable regarding COVID-19 transmissions as compared with temperature and relative humidity. Maximum COVID-19 incidents were observed at 4-9 g/m3 AH and 3-17 °C temperature. | Bukhari and Jameel |
| USA | January 1 to April 9, 2020 | Absolute humidity (AH) and temperature | AH is more significant weather variable and maximum COVID-19 cases reported within 4–6 g/m3 AH and 4–11 °C temperature. | Gupta et al. |
| All the affected countries | January, 2020 | Temperature | Temperature influenced the COVID-19 infection intensities. | Wang et al. |
| China | January, 2020 | Absolute humidity (AH) and temperature | COVID-19 outbreak was associated with AH and temperature. However, weather changes would not affect infection transmission intensity and exponential increase in positive cases. | Luo et al. |
| Wuhan, China | Historical datasets of 2002 to 2003 and 2015 to 2019 | Temperature, humidity and precipitation | 13–24 °C temperature, 50%-80% of humidity and < 30 mm rainfall/month facilitates the survival of 2019-nCoV. Cold climate may eliminate the virus. | Bu et al. |
| Entire globe | January 22, to April 6, 2020 | Temperature, precipitation, wind speed, solar radiation, and water vapour pressure | Warming velocity and precipitation pattern mostly influenced the COVID-19 transmissions as compared with temperature. | Chiyomaru and Takemoto |
| Brazil | February 27 to April 1, 2020 | Temperature | Negative linear relationship was analysed between a specific temperature range (16.8 to 27.4 °C) and COVID-19 daily infections. | Prata et al. |
| China | January 23, to February 29, 2020 | Temperature | Linear relationship was found in between daily COVID-19 positive cases and average temperature (threshold limit 3 °C). 4.861% rise in daily positive cases was also recorded with increase in 1 °C temperature. | Xie and Zhu |
| Iran | February 19 to March 22, 2020 | Temperature, precipitation, humidity, wind speed, and solar radiation | Low wind speed, less amount of solar radiation and humidity promotes the survival rate of COVID-19 virus. | Ahmadia et al. |
| China | January and February, 2020 | Weather data (temperature, solar radiation and precipitation | Influences of weather on COVID-19 survival and transmissions are limited which does not refer the extinction of the pandemic during summer. | Byass |
| Italy | February 1 to April 1, 2020 | Average temperature, moisture %, wind speed, days of rain and fog | Low wind speed, High moisture % and no. of fog days, high air pollution level accelerates transmission dynamics of viral infectivity. | Coccia |
| Iran | February 15 to March 22, 2020 | Average temperature, population size | Average temperature has low sensibility while population size has high sensitivity to the transmission rate of COVID-19. No evidence of lower transmission rate in warmer climate in comparison with cold/moderate climates was obtained. | Jahangiri et al. |
Presence of SARS-CoV-2 in wastewater
| Study area | Water matrix | Sample volume | Time span | Virus concentration and detection method | Inferences | References |
|---|---|---|---|---|---|---|
| Massachusetts, USA | Sewage | 18th March to 25th March 2020 | Initial testing was done with PCR using primers specific for the SARS-CoV-2 S gene followed by US CDC primer/probe sets targeting the N1, N2, and N3 loci of the SARS-CoV-2 nucleocapsid gene | SARS-CoV-2 was detected in all the 10 samples with approximately 100 genomic copies/ml | Wu et al. | |
| Southeast Queensland, Australia | Untreated wastewater (sewage) | 100–200 ml | 24th February 2020 to 1st May 2020 | Viruses was concentrated via two methods: (i) direct RNA extraction from electronegative membranes and (ii) ultrafiltration followed by detection with RT-qPCR with two different primer-probe sets for nucleocapsid protein gene | Out of nine samples, two were tested positive; one positivity for each concentration method (not the same sample) but with only one set of primers and at very low titres: 1.2 and 1.9 genomic copies/100 ml | Ahmed et al. |
| Region of Murcia (Spain) | Wastewater | 200 ml | 12 March to 14 April 2020 | Aluminium hydroxide adsorption-precipitation concentration method was used and RT-qPCR diagnostic panel validated by US CDC was used for detection | SARS-CoV-2 RNA was detected in two out of eighteen secondary water samples and all twelve tertiary water samples were tested as negative | Randazzo et al. |
| Paris, France | Raw and treated wastewater | 5th March to 23rd April 2020 | Viral concentrate was lysed and extracted using PowerFecal Pro kit (QIAGEN) on QIAsymphony automated extractor; Confirmed by RT-qPCR on viral RdRp gene | All the samples tested positive for SARS-CoV-2 genomes | Wurtzer et al. | |
| Milan and Rome, Italy | Influent sewage | 250 ml | February and April 2020 | Concentration was done using two-phase (PEG-dextran method) separation; developed novel nested PCR assay specific for SARS-CoV-2 analysis | 50% samples were tested positive and one of them was present in the sample that was collected just a few days after the first case of SARS-CoV-2 in Italy. | La Rosa et al. |
| Bozeman, Montana, USA | Raw sewage | 500 ml | 23rd March to 27th March, 2020; 30th March to 3rd April 2020 | The samples were concentrated with Corning Spin-X UF concentrators & RNeasy Mini Kit extracted RNA. RT-qPCR was done using N1 and N2 primer pairs and probes from 2019-nCoV CDC EUA Kit | All the seven samples tested positive for SARS-CoV-2. Composite sampling is suggested as the most reliable method for calculating viral conc. In water over time. | Nemudryi et al. ( |
| Netherlands | 100–200 ml | 5th February to 16th March 2020 | Samples were filtered and concentrated by centrifugation. Four primer sets were selected, i.e. N1–N3 for nucleocapsid protein gene and envelope protein (E) gene against two separate SARS-CoV-2 genes | 77.8% samples foundpositive after reporting of the first case of COVID 19 in Netherlands. | Medema et al. ( | |
| Yamanshi Prefecture, Japan | Wastewater and river water | 200–5,000 ml | 17th March to 7th May 2020 | Concentration and extraction was done using electronegative membrane-vortex (EMV) method and adsorption-direc tRNA extraction method | SARS-CoV-2 detected in 20% of the sec. wastewater with a conc. of 2.4 × 103 copies/L. All the sample of influent and river were tested negative. EMV method was found superior | Haramoto et al. |
*The data has been retrieved from medRxiv as preliminary reports, which had not yet been peer-reviewed