Spatiotemporal Spread Research Articles

Detection of spreading depolarization events and spatiotemporal analysis for advancing stroke therapy.

While the presence of spreading depolarization (SD) and associated spreading depression have been well studied and known to be associated with post-ischemic brain damage, the spatiotemporal spread of these events from the site of injury is not well understood. With the recent development of high-density micro-electrocorticographic (ECoG) electrode arrays, monitoring the spread of the depolarizing events and associated depression is possible. The goal of this work is to define the electrocorticographic features of SD and associated depression across the multichannel array and search for patterns in these features that emerge across both space and time. We present the spatial distribution of features found from chronic ECoG recordings acquired from awake behaving rats induced with a rodent model of stroke. SD events were detected with an unsupervised algorithm that searched for a stereotyped pattern in the first derivative of the ECoG. The algorithm yielded a 58% correct detection rate on average across four rats, and a 36% false positive rate. We defined key electrophysiological features and mapped them onto the physical brain regions using MATLAB, such as the peak-to-peak amplitude of each SD event, the width (or duration) of the SD event, direct current (DC) level, and average rate of decline in the signal baseline. We performed k-means clustering to the activity in this feature space which yielded three contiguous regions in physical space. The elbow optimization method was applied to a distortion metric and indicated that 3 clusters was optimal. These findings motivate us to conduct future studies that would verify whether these 3 clusters in electrode-space correspond to immunohistochemically defined regions of tissue health, namely, infarct, penumbra, and healthy tissue. Clinical Relevance- The extent and severity of damage that stroke ultimately causes is suspected to be related to the progression of spreading depolarization and associated depression. An understanding of how the features of these electrophysiological events progress across the brain and over time is an important step toward eventual development of closed-loop therapies which limit and minimize the long-term effects of stroke.

Probing Neuropeptide Volume Transmission In Vivo by Simultaneous Near‐Infrared Light‐Triggered Release and Optical Sensing**

AbstractNeuropeptides are abundant signaling molecules in the central nervous system. Yet remarkably little is known about their spatiotemporal spread and biological activity. Here, we developed an integrated optical approach using Plasmonic nAnovesicles and cell‐based neurotransmitter fluorescent engineered reporter (CNiFER), or PACE, to probe neuropeptide signaling in the mouse neocortex. Small volumes (fL to pL) of exogenously supplied somatostatin‐14 (SST) can be rapidly released under near‐infrared light stimulation from nanovesicles implanted in the brain and detected by SST2 CNiFERs with nM sensitivity. Our measurements reveal reduced but synchronized SST transmission within 130 μm, and markedly smaller and delayed transmission at longer distances. These measurements enabled a quantitative estimation of the SST loss rate due to peptide degradation and binding. PACE offers a new tool for determining the spatiotemporal scales of neuropeptide volume transmission and signaling in the brain.

Probing Neuropeptide Volume Transmission In Vivo by Simultaneous Near-Infrared Light-Triggered Release and Optical Sensing.

Neuropeptides are abundant signaling molecules in the central nervous system. Yet remarkably little is known about their spatiotemporal spread and biological activity. Here, we developed an integrated optical approach using Plasmonic nAnovesicles and cell-based neurotransmitter fluorescent engineered reporter (CNiFER), or PACE, to probe neuropeptide signaling in the mouse neocortex. Small volumes (fL to pL) of exogenously supplied somatostatin-14 (SST) can be rapidly released under near-infrared light stimulation from nanovesicles implanted in the brain and detected by SST2 CNiFERs with nM sensitivity. Our measurements reveal reduced but synchronized SST transmission within 130 μm, and markedly smaller and delayed transmission at longer distances. These measurements enabled a quantitative estimation of the SST loss rate due to peptide degradation and binding. PACE offers a new tool for determining the spatiotemporal scales of neuropeptide volume transmission and signaling in the brain.

The spread of Lessepsian fish does not track native temperature conditions

Abstract Global warming is enabling tropical invaders to expand into regions, where unsuitable climate conditions used to prevent the survival or reproduction. Here, using the occurrence data of 22 Lessepsian fish in the Mediterranean Sea, we aimed at (1) gaining a deeper understanding of temperature match dynamics over the spatio-temporal spread of each invasion event and (2) testing the hypothesis that successful invasions require a temperature match, as assumed in several bioinvasion risk assessments. For each species, we identified the areas of the Mediterranean lacking a temperature match with native ranges over the past six decades. We found that some species, particularly recent invaders, have spread far beyond temperature matching conditions. The populations at the expanding edge of the range are often found in sites that have experienced increases in temperature. Temperature match positively correlates with species spread rate and after 1990, higher temperatures in the Mediterranean provided analogous conditions for a growing number of non-indigenous species. Our results warn against the general use of temperature-based climate matching in bioinvasion risk and provide guidance for the application of species distribution models that incorporate temperature to marine bioinvasion research.

Applications of Sentinel-2 satellite data for spatio-temporal mapping of deep pools for monitoring the riverine connectivity and assessment of ecological dynamics: a case from Godavari, a tropical river in India (2016-2021).

Rivers are dynamic ecosystems with diverse habitats that require ample connectivity to ensure the flow of ecosystem services, thus empowering the sustainable development of an entire basin. Geo-spatial tools offer powerful prospects for monitoring of aquatic ecosystems. The usefulness of Sentinel-2 datasets to assess river connectivity has been explored for an un-gauged seasonal river system. The present study was undertaken in five ecologically unique river reaches viz. Wainganga, Wardha, Pranhita, Godavari-mid and Manair in Godavari Basin in the Indian Deccan Plateau to map water spread dynamics at various time scales, i.e.,fortnightly, monthly, seasonal, annual and demi-decadal during 2016-2021. The maximum value of perennial water spread per square kilometre of total floodplain area (2016-2021), determined using Sentinel-2 imageries, was observed in river Wardha (0.18) followed by Pranhita (0.12) and Wainganga (0.11). The water spread showed a decreasing trend, while the number of patches in the river corridor increased over time from post-monsoon to pre-monsoon season. The copious perennial habitat with relatively larger patches, incessant flow in river Pranhita and obstructed flow, large-sized patches reported in river Wardha during summer months, hold importance in terms of providing refuge to aquatic biota. This study provides evidence for the impact of water projects on spatio-temporal water spread dynamics in Godavari Basin. The demonstrated utility of Sentinel-2 imagery coupled with gauge station measurements for river continuity assessment and deep pool mapping would aid in enhancing our understanding on environmental flow at a spatial scale, which in turn would aid in effective river management to achieve the Sustainable Development Goals. The implications of this study for sustainable environmental management and limitations are also discussed.

A Biosensor Platform for Point-of-Care SARS-CoV-2 Screening.

The COVID-19 pandemic remains a constant threat to human health, the economy, and social relations. Scientists around the world are constantly looking for new technological tools to deal with the pandemic. Such tools are the rapid virus detection tests, which are constantly evolving and optimizing. This paper presents a biosensor platform for the rapid detection of spike protein both in laboratory conditions and in swab samples from hospitalized patients. It is a continuation and improvement of our previous work and consists of a microcontroller-based readout circuit, which measures the capacitance change generated in an interdigitated electrode transducer by the presence either of sole spike protein or the presence of SARS-CoV-2 particles in swab samples. The circuit efficiency is calibrated by its correlation with the capacitance measurement of an LCR (inductance (L), capacitance (C), and resistance (R)) meter. The test result is made available in less than 2 min through the microcontroller’s LCD (liquid-crystal display) screen, whereas at the same time, the collected data are sent wirelessly to a mobile application interface. The novelty of this research lies in the potential it offers for continuous and effective screening of SARS-CoV-2 patients, which is facilitated and enhanced, providing big data statistics of COVID-19 in terms of space and time. This device can be used by individuals for SARS-CoV-2 testing at home, by health professionals for patient monitoring, and by public health agencies for monitoring the spatio-temporal spread of the virus.

Temporal and Spatial Evolution of the African Swine Fever Epidemic in Vietnam

African swine fever (ASF) is a severe infectious disease affecting domestic and wild suids. Spatiotemporal dynamics analysis of the ASF is crucial to understanding its transmission. The ASF broke out in Vietnam in February 2019. The research on the spatiotemporal evolution characteristics of ASF in Vietnam is lacking. Spatiotemporal statistical methods, including direction analysis, spatial autocorrelation analysis, and spatiotemporal scan statistics were used to reveal the dynamics of the spatial diffusion direction and spatiotemporal aggregation characteristics of ASF in Vietnam. According to the cessation of the epidemic, it was divided into three phases: February to August 2019 (phase 1), April to December 2020 (phase 2), and January 2021 to March 2022 (phase 3). The ASF showed a significant spread trend from north to south in phase 1. The occurrence rate of the ASF aggregated spatially in phase 1 and became random in phases 2 and 3. The high−high ASF clusters (the province was a high cluster and both it and its neighbors had a high ASF occurrence rate) were concentrated in the north in phases 1 and 2. Four spatiotemporal high-risk ASF clusters were identified with a mean radius of 121.88 km. In general, there were significant concentrated outbreak areas and directional spread in the early stage and small-scale, high-frequency, and randomly scattered outbreaks in the later stage. The findings could contribute to a deeper understanding of the spatiotemporal spread of the ASF in Vietnam.

Spatio-temporal spread of COVID-19: Comparison of the inhomogeneous SEPIR model and data from South Carolina.

During the COVID-19 pandemic authorities have been striving to obtain reliable predictions for the spreading dynamics of the disease. We recently developed a multi-“sub-populations” (multi-compartments: susceptible, exposed, pre-symptomatic, infectious, recovered) model, that accounts for the spatial in-homogeneous spreading of the infection and shown, for a variety of examples, how the epidemic curves are highly sensitive to location of epicenters, non-uniform population density, and local restrictions. In the present work we test our model against real-life data from South Carolina during the period May 22 to July 22 (2020). During this period, minimal restrictions have been employed, which allowed us to assume that the local basic reproduction number is constant in time. We account for the non-uniform population density in South Carolina using data from NASA’s Socioeconomic Data and Applications Center (SEDAC), and predict the evolution of infection heat-maps during the studied period. Comparing the predicted heat-maps with those observed, we find high qualitative resemblance. Moreover, the Pearson’s correlation coefficient is relatively high thus validating our model against real-world data. We conclude that the model accounts for the major effects controlling spatial in-homogeneous spreading of the disease. Inclusion of additional sub-populations (compartments), in the spirit of several recently developed models for COVID-19, can be easily performed within our mathematical framework.

Redefining the timing and circumstances of the chicken's introduction to Europe and north-west Africa

Little is known about the early history of the chicken (Gallus gallus domesticus), including the timing and circumstances of its introduction into new cultural environments. To evaluate its spatio-temporal spread across Eurasia and north-west Africa, the authors radiocarbon dated 23 chicken bones from presumed early contexts. Three-quarters returned dates later than those suggested by stratigraphy, indicating the importance of direct dating. The results indicate that chickens did not arrive in Europe until the first millennium BC. Moreover, a consistent time-lag between the introduction of chickens and their consumption by humans suggests that these animals were initially regarded as exotica and only several centuries later recognised as a source of ‘food’.

Wastewater Reveals the Spatiotemporal Spread of SARS-CoV-2 in the Canton of Ticino (Switzerland) during the Onset of the COVID-19 Pandemic.

Wastewater-based epidemiology (WBE) has emerged as an effective tool for monitoring SARS-CoV-2 dynamics during the COVID-19 pandemic. Here, we add a spatial component to WBE and use it to investigate SARS-CoV-2 spread in the canton of Ticino during the onset of the pandemic in Switzerland (end of February 2020 to beginning of March 2020). Ticino is located at the border to Northern Italy, where a large COVID-19 outbreak occurred in February 2020. Not surprisingly, Ticino was the site of the first clinically confirmed COVID-19 case in Switzerland. We retrospectively analyzed daily influent samples from nine wastewater treatment plants in Ticino that jointly cover an area of 20 km × 60 km and 351,000 people (>99% of the population). Our result is a fine-grained view of the spatiotemporal evolution of the COVID-19 pandemic in this canton. The wastewater analysis revealed that by February 29, 2020, SARS-CoV-2 had already spread to all catchments. At the same time, only four individual cases had been clinically confirmed across the region served by the treatment plants investigated. Our results demonstrate that WBE could serve as a versatile tool to monitor the introduction and spread of an infectious agent on a regional scale. To fully exploit its utility, WBE should be implemented in real time and become an integral part of future disease surveillance efforts.

The effects of geographical distributions of buildings and roads on the spatiotemporal spread of canine rabies: An individual-based modeling study.

Rabies is a fatal disease that has been a serious health concern, especially in developing countries. Although rabies is preventable by vaccination, the spread still occurs sporadically in many countries, including Thailand. Geographical structures, habitats, and behaviors of host populations are essential factors that may result in an enormous impact on the mechanism of propagation and persistence of the disease. To investigate the role of geographical structures on the transmission dynamics of canine rabies, we developed a stochastic individual-based model that integrates the exact configuration of buildings and roads. In our model, the spatial distribution of dogs was estimated based on the distribution of buildings, with roads considered to facilitate dog movement. Two contrasting areas with high- and low-risk of rabies transmission in Thailand, namely, Hatyai and Tepha districts, were chosen as study sites. Our modeling results indicated that the distinct geographical structures of buildings and roads in Hatyai and Tepha could contribute to the difference in the rabies transmission dynamics in these two areas. The high density of buildings and roads in Hatyai could facilitate more rabies transmission. We also investigated the impacts of rabies intervention, including reducing the dog population, restricting owned dog movement, and dog vaccination on the spread of canine rabies in these two areas. We found that reducing the dog population alone might not be sufficient for preventing rabies transmission in the high-risk area. Owned dog confinement could reduce more the likelihood of rabies transmission. Finally, a higher vaccination coverage may be required for controlling rabies transmission in the high-risk area compared to the low-risk area.

Research on Spatial-temporal Spread and Risk Profile of the COVID-19 Epidemic Based on Mobile Phone Trajectory Data.

The COVID-19 epidemic poses a significant challenge to the operation of society and the resumption of work and production. How to quickly track the resident location and activity trajectory of the population, and identify the spread risk of the COVID-19 in geospatial space has important theoretical and practical significance for controlling the spread of the virus on a large scale. In this study, we take the geographical community as the research object, and use the mobile phone trajectory data to construct the spatiotemporal profile of the potential high-risk population. First, by using the spatiotemporal data collision method, identify, and recover the trajectories of the people who were in the same area with the confirmed patients during the same time. Then, based on the range of activities of both cohorts (the confirmed cases and the potentially infected groups), we analyze the risk level of the relevant places and evaluate the scale of potential spread. Finally, we calculate the probability of infection for different communities and construct the spatiotemporal profile for the transmission to help guide the distribution of preventive materials and human resources. The proposed method is verified using survey data of 10 confirmed cases and statistical data of 96 high-risk neighborhoods in Chengdu, China, between 15 January 2020 and 15 February 2020. The analysis finds that the method accurately simulates the spatiotemporal spread of the epidemic in Chengdu and measures the risk level in specific areas, which provides an objective basis for the government and relevant parties to plan and manage the prevention and control of the epidemic.

Regional land cover changes of the last 6,500 years in middle and southern subtropical China

Our knowledge about the Holocene vegetation changes in southern China has been based on the inference from pollen percentages. However, the relationship between pollen percentages and vegetation is non-linear. This may lead to significant bias in real vegetation cover and limit our understanding of land cover changes and its relation to climate and human activities. Here we quantitatively reconstruct past regional land-cover changes by applying the REVEALS model to four pollen records collected from two study areas in southern and middle subtropical zones. The two study areas, representing the Pearl River Basin (PRB) and Wuyi Mountain (WYM), are located in spatial gradients, thus help to elucidate not only the climate forcing, but also the spatio-temporal spread of agriculture in this region. The REVEALS reconstruction shows a clear difference from original pollen percentages and is considered more realistic in depicting regional vegetation changes. Based on more reasonable reconstruction result by excluding wetland-associated taxa, i.e., Cyperaceae and Alnus, we found a gradual decline of forest covers since the middle Holocene which may be related to the reduced rainfall in response to the weakening East Asian Summer Monsoon. The human impact on the forest cover was identified by an abrupt increase of Poaceae cover at 3500–2700 cal yr B.P. in the PRB area, which appeared later in the WYM mountainous area. Thus, our study reveals a progressive expansion of anthropogenic activities from the coastal lowlands to mountainous areas.

Monitoring of SARS-CoV-2 in sewersheds with low COVID-19 cases using a passive sampling technique

Monitoring SARS-CoV-2 RNA in sewer systems, upstream of a wastewater treatment plant, is an effective approach for understanding potential COVID-19 transmission in communities with higher spatial resolutions. Passive sampling devices provide a practical solution for frequent sampling within sewer networks where the use of autosamplers is not feasible. Currently, the design of upstream sampling is impeded by limited understanding of the fate of SARS-CoV-2 RNA in sewers and the sensitivity of passive samplers for the number of infected individuals in a catchment. In this study, passive samplers containing electronegative membranes were applied for at least 24-h continuous sampling in sewer systems. When monitoring SARS-CoV-2 along a trunk sewer pipe, we found RNA signals decreased proportionally to increasing dilutions, with non-detects occurring at the end of pipe. The passive sampling membranes were able to detect SARS-CoV-2 shed by >2 COVID-19 infection cases in 10,000 people. Moreover, upstream monitoring in multiple sewersheds using passive samplers identified the emergence of SARS-CoV-2 in wastewater one week ahead of clinical reporting and reflected the spatiotemporal spread of a COVID-19 cluster within a city. This study provides important information to guide the development of wastewater surveillance strategies at catchment and subcatchment levels using different sampling techniques.

Relationships between socio-demographic structure and spatio-temporal distribution patterns of COVID-19 cases in Istanbul, Turkey

ABSTRACT This study aims to find out specific relationships between socio-demographic and spatio-temporal distribution patterns of COVID-19 cases. Istanbul being one of the most dynamic and overpopulated cities in Turkey is chosen as the case area. The study explores the spatio-temporal spread pattern of COVID-19 between 24 September and 12 December 2020 in 960 neighbourhoods of Istanbul using spatial statistical analysis. Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) methods are used to explain how socio-demographic structure and intensity of COVID-19 cases are related. The results of the study show that gender, household size, and population density are important drivers of exposure to COVID-19. Education level is also found statistically significant though having a weaker effect on spatio-temporal distribution pattern of COVID-19. It is anticipated that the findings of this study will be used by the decision-makers to take action to control the spread of the COVID-19 pandemic – and any other upcoming and unexpected diseases – and to improve the existing conditions to overcome such vulnerabilities to possible risk factors.

Zero-State Coupled Markov Switching Count Models for Spatio-Temporal Infectious Disease Spread

AbstractSpatio-temporal counts of infectious disease cases often contain an excess of zeros. With existing zero-inflated count models applied to such data it is difficult to quantify space-time heterogeneity in the effects of disease spread between areas. Also, existing methods do not allow for separate dynamics to affect the reemergence and persistence of the disease. As an alternative, we develop a new zero-state coupled Markov switching negative binomial model, under which the disease switches between periods of presence and absence in each area through a series of partially hidden nonhomogeneous Markov chains coupled between neighbouring locations. When the disease is present, an autoregressive negative binomial model generates the cases with a possible zero representing the disease being undetected. Bayesian inference and prediction is illustrated using spatio-temporal counts of dengue fever cases in Rio de Janeiro, Brazil.

Assessing the Spatiotemporal Spread Pattern of the COVID-19 Pandemic in Malaysia.

IntroductionThe unprecedented COVID-19 pandemic has greatly affected human health and socioeconomic backgrounds. This study examined the spatiotemporal spread pattern of the COVID-19 pandemic in Malaysia from the index case to 291,774 cases in 13 months, emphasizing on the spatial autocorrelation of the high-risk cluster events and the spatial scan clustering pattern of transmission.MethodologyWe obtained the confirmed cases and deaths of COVID-19 in Malaysia from the official GitHub repository of Malaysia's Ministry of Health from January 25, 2020 to February 24, 2021, 1 day before the national vaccination program was initiated. All analyses were based on the daily cumulated cases, which are derived from the sum of retrospective 7 days and the current day for smoothing purposes. We examined the daily global, local spatial autocorrelation and scan statistics of COVID-19 cases at district level using Moran's I and SaTScan™.ResultsAt the initial stage of the outbreak, Moran's I index > 0.5 (p < 0.05) was observed. Local Moran's I depicted the high-high cluster risk expanded from west to east of Malaysia. The cases surged exponentially after September 2020, with the high-high cluster in Sabah, from Kinabatangan on September 1 (cumulative cases = 9,354; Moran's I = 0.34; p < 0.05), to 11 districts on October 19 (cumulative cases = 21,363, Moran's I = 0.52, p < 0.05). The most likely cluster identified from space-time scanning was centered in Jasin, Melaka (RR = 11.93; p < 0.001) which encompassed 36 districts with a radius of 178.8 km, from November 24, 2020 to February 24, 2021, followed by the Sabah cluster.Discussion and ConclusionBoth analyses complemented each other in depicting underlying spatiotemporal clustering risk, giving detailed space-time spread information at district level. This daily analysis could be valuable insight into real-time reporting of transmission intensity, and alert for the public to avoid visiting the high-risk areas during the pandemic. The spatiotemporal transmission risk pattern could be used to monitor the spread of the pandemic.

The COVID-19 wave in Belgium during the Fall of 2020 and its association with higher education.

Soon after SARS-CoV-2 emerged in late 2019, Belgium was confronted with a first COVID-19 wave in March-April 2020. SARS-CoV-2 circulation declined in the summer months (late May to early July 2020). Following a successfully trumped late July-August peak, COVID-19 incidence fell slightly, to then enter two successive phases of rapid incline: in the first half of September, and then again in October 2020. The first of these coincided with the peak period of returning summer travelers; the second one coincided with the start of higher education's academic year. The largest observed COVID-19 incidence occurred in the period 16-31 October, particularly in the Walloon Region, the southern, French-speaking part of Belgium. We examine the potential association of the higher education population with spatio-temporal spread of COVID-19, using Bayesian spatial Poisson models for confirmed test cases, accounting for socio-demographic heterogeneity in the population. We find a significant association between the number of COVID-19 cases in the age groups 18-29 years and 30-39 years and the size of the higher education student population at the municipality level. These results can be useful towards COVID-19 mitigation strategies, particularly in areas where virus transmission from higher education students into the broader community could exacerbate morbidity and mortality of COVID-19 among populations with prevalent underlying conditions associated with more severe outcomes following infection.

Spatiotemporal spread of Fermi-edge singularity as time-delayed interaction and impact on time-dependent RKKY-type coupling

Fermi-edge singularity and Anderson's orthogonality catastrophe are paradigmatic examples of non-equilibrium many-body physics in conductors, appearing after a quench is created by the sudden change of a localised potential. We investigate if the signal carried by the quench can be used to transmit a long ranged interaction, reminiscent of the RKKY interaction, but with the inclusion of the full many-body propagation over space and time. We calculate the response of a conductor to two quenches induced by localised states at different times and locations. We show that building up and maintaining coherence between the localised states is possible only with finely tuned interaction between the localised states and the conductor. This puts bounds to the use of time controlled RKKY type interactions and may limit the speed at which some quantum gates could operate.

Spatiotemporal Analysis for COVID-19 Delta Variant Using GIS-Based Air Parameter and Spatial Modeling.

The coronavirus disease of 2019 (COVID-19) pandemic is currently a global challenge, with 210 countries, including Indonesia, seeking to minimize its spread. Therefore, this study aims to determine the spatiotemporal spread pattern of this virus in Surabaya using various data on confirmed cases from 28 April to 26 October 2021. It also aims to determine the relationship between pollutant parameters, such as carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3), as well as the government’s high social restrictions policy in Java-Bali. Several methods, such as the weighted mean center, directional distribution, Getis–Ord Gi*, Moran’s I, and geographically weighted regression, were used to identify the spatial spread pattern of the virus. The weighted mean center indicated that the epicenter location of the outbreak moved randomly. The directional distribution demonstrated a decrease of 21 km2 at the end of the study phase, which proved that its spread has significantly reduced in Surabaya. Meanwhile, the Getis–Ord Gi* results demonstrated that the eastern and southern parts of the study region were highly infected. Moran’s I demonstrate that COVID-19 cases clustered during the spike. The geographically weighted regression model indicated a number of influence zones in the northeast, northwest, and a few in the southwest parts at the peak of R2 0.55. The relationship between COVID-19 cases and air pollution parameters proved that people living at the outbreak’s center have low pollution levels due to lockdown. Furthermore, the lockdown policy reduced CO, NO2, SO2, and O3. In addition, increase in air pollutants; namely, NO2, CO, SO2 and O3, was recorded after 7 weeks of lockdown implementation (started from 18 August).