COVID-19, climate shocks, and food security linkages: evidence and perceptions from smallholder farming communities in Tanzania

Insights on the indirect effects of the COVID-19 pandemic are critical for designing and implementing policies to alleviate the food security burden it may have caused, and for bolstering rural communities against similar macroeconomic shocks in the future. Yet estimating the causal effects of the pandemic is difficult due to its ubiquitous nature and entanglement with other shocks. In this descriptive study, we combine high-resolution satellite imagery to control for plot-level rainfall with household socio-economic panel data from 2014, 2016, 2019 and 2020, to differentiate the effect of the pandemic from climatic shocks on food security in Morogoro, Tanzania. We find evidence of decreased incomes, increased prices of staple foods, and increased food insecurity in 2020 relative to previous years, and link these changes to the pandemic by asking households about their perceptions of COVID-19. Respondents overwhelmingly attribute economic hardships to the pandemic, with perceived impacts differing by asset level.

Sustainability assessment of crops' production in India: empirical evidence from ARDL-ECM approach

PurposeThe study aims to evaluate the long- vs short-run relationships between crops' production (output) and crops' significant inputs such as land use, agricultural water use (AWU) and gross irrigated area in India during the period 1981–2018.Design/methodology/approachThe study applied the autoregressive distributed lag (ARDL) bounds testing approach to estimate the co-integration among the variables. The study uses the error correction model (ECM), which integrates the short-run dynamics with the long-run equilibrium.FindingsThe ARDL bounds test of co-integration confirms the strong evidence of the long-run relationship among the variables. Empirical results show the positive and significant relationship of crops' production with land use and gross irrigated area. The statistically significant error correction term (ECT) validates the speed of adjustment of the empirical models in the long-run.Research limitations/implicationsThe study suggests that the decision-makers must understand potential trade-offs between human needs and environmental impacts to ensure food for the growing population in India.Originality/valueFor a clear insight into the impact of climate change on crops' production, the current study incorporates the climate variables such as annual rainfall, maximum temperature and minimum temperature. Further, the study considered agro-chemicals, i.e. fertilizers and pesticides, concerning their negative impacts on increased agricultural production and the environment.

Predictors of Evacuation Rates during Hurricane Laura: Weather Forecasts, Twitter, and COVID-19

Machine learning was applied to predict evacuation rates for all census tracts affected by Hurricane Laura. The evacuation ground truth was derived from cellular telephone–based mobility data. Twitter data, census data, geographical data, COVID-19 case rates, the social vulnerability index from the Centers for Disease Control and Prevention (CDC)/Agency for Toxic Substances and Disease Registry (ATSDR), and relevant weather and physical data were used to do the prediction. Random forests were found to perform well, with a mean absolute percent error of 4.9% on testing data. Feature importance for prediction was analyzed using Shapley additive explanations and it was found that previous evacuation, rainfall forecasts, COVID-19 case rates, and Twitter data rank highly in terms of importance. Social vulnerability indices were also found to show a very consistent relationship with evacuation rates, such that higher vulnerability consistently implies lower evacuation rates. These findings can help with hurricane evacuation preparedness and planning as well as real-time assessment. © 2023 American Meteorological Society.

Improving Short-Term QPF Using Geostationary Satellite All-Sky Infrared Radiances: Real-Time Ensemble Data Assimilation and Forecast during the PRECIP 2020 and 2021 Experiments

The Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) aims to improve our understanding of extreme rainfall processes in the East Asian summer monsoon. A convection-permitting ensemble-based data assimilation and forecast system (the PSU WRF-EnKF system) was run in real time in the summers of 2020–21 in advance of the 2022 field campaign, assimilating all-sky infrared (IR) radiances from the geostationary Himawari-8 and GOES-16 satellites, and providing 48-h ensemble forecasts every day for weather briefings and discussions. This is the first time that all-sky IR data assimilation has been performed in a real-time forecast system at a convection-permitting resolution for several seasons. Compared with retrospective forecasts that exclude all-sky IR radiances, rainfall predictions are statistically significantly improved out to at least 4–6 h for the real-time forecasts, which is comparable to the time scale of improvements gained from assimilating observations from the dense ground-based Doppler weather radars. The assimilation of all-sky IR radiances also reduced the forecast errors of large-scale environments and helped to maintain a more reasonable ensemble spread compared with the counterpart experiments that did not assimilate all-sky IR radiances. The results indicate strong potential for improving routine short-term quantitative precipitation forecasts using these high-spatiotemporal-resolution satellite observations in the future.Significance StatementDuring the summers of 2020/21, the PSU WRF-EnKF data assimilation and forecast system was run in real time in advance of the 2022 Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP), assimilating all-sky (clear-sky and cloudy) infrared radiances from geostationary satellites into a numerical weather prediction model and providing ensemble forecasts. This study presents the first-of-its-kind systematic evaluation of the impacts of assimilating all-sky infrared radiances on short-term qualitative precipitation forecasts using multiyear, multiregion, real-time ensemble forecasts. Results suggest that rainfall forecasts are improved out to at least 4–6 h with the assimilation of all-sky infrared radiances, comparable to the influence of assimilating radar observations, with benefits in forecasting large-scale environments and representing atmospheric uncertainties as well.

Effects of Temperature and Water Types on the Decay of Coronavirus: A Review

The analysis of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) gene copy numbers in wastewater samples can provide quantitative information on Coronavirus Disease-19 (COVID-19) cases within a sewer catchment. However, many wastewater-based epidemiology (WBE) studies have neglected virus decay during the wastewater transportation process in sewers while back-calculating COVID-19 prevalence. Among various sewer condition parameters, wastewater temperature and dilution by fresh/saltwater infiltration may result in a significant change to the virus decay, in terms of both infectivity and Ribonucleic Acid (RNA). This paper reviewed the literature to identify and discuss the effects of temperature and water types (i.e., wastewater, freshwater, and seawater) on coronavirus decay based on the decay rate constants that were collected from published papers. To evaluate the importance of virus decay, a sensitivity analysis was then conducted with decay rates of SARS-CoV-2 RNA based on a WBE back-calculation equation. Finally, the decay rates of coronavirus in wastewater were also compared with those of other viruses to further understand the difference among virus species. The decay of SARS-CoV-2 RNA was found to be less impacted by temperature variation than viable coronaviruses. Nevertheless, WBE back-calculation was still sensitive to the RNA decay rates increased by warm wastewater (i.e., over 26 °C), which could lead to a two-times higher relative variance in estimated COVID-19 prevalence, considering the wastewater temperature variation between 4 and 37 °C in a sewer catchment with a 12-h hydraulic retention time. Comparatively, the sensitivity of the WBE estimation to the enveloped SARS-CoV-2 was greater than nonenveloped enteric viruses, which were less easily degradable in wastewater. In addition, wastewater dilution by stormwater inflow and accompanied cold weather might alleviate the decay of coronavirus infectivity, thus increasing the potential risk of COVID-19 transmission through wastewater. Overall, this paper aims to better understand the impact of in-sewer processes on coronavirus decay and its potential implications for WBE. The outcome could quantitatively inform WBE and improve awareness of the increased risk of COVID-19 infection via wastewater during heavy rainfall events. Given the identified scarcity of data available for coronavirus decay in salt water or with chemical additions, future research on the fate of SARS-CoV-2 subjected to chemical dosing for sewer or wastewater treatment plant operations is recommended. © 2023 by the authors.

EVALUATION OF THE EFFECT OF RAINFALL ON COVID-19 TRANSMISSION IN TERENGGANU, MALAYSIA

One of the variables leading to the global spread of COVID-19 cases is the weather, which includes temperature and air quality. In this study, an investigation of the association between precipitation and COVID-19 cases was conducted to provide useful information on the possibility of this climate factor (precipitation) on the progression of COVID-19 cases for an appropriate management strategy. Secondary COVID-19 and rainfall data obtained from the Ministry of Health and the Meteorological Department in Malaysia were used for the study. The collected data were subjected to Pearson correlation analysis. The results of this study showed that both rainy days and rainfall amount were insignificant to COVID-19 cases, indicating that rainfall amount was not associated with COVID-19 transmission in Terengganu, Malaysia. Thus, this discovery could be used to inform individual and COVID-19 supervisors and the government as it prepares for the new weather season. © Penerbit UMT

TIME-SERIES ANALYSIS OF AIR QUALITY IN MAJOR CITIES IN THE PHILIPPINES DURING THE COVID-19 PANDEMIC THROUGH SENTINEL-5

Air pollution causes respiratory ailments and drives climate change. Air quality is driven by emissions from various sources, weather patterns, and transport of pollutants. Satellite analysis of pollutants in the atmosphere can provide temporally consistent and spatially wide measurements. In this study, the monthly concentrations of Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Carbon Monoxide (CO), and Ozone (O3) from the Sentinel-5 Tropospheric Monitoring Instrument (TROPOMI) were analyzed in four major cities in the Philippines, representing different climate types. Satellite-based measurements of land surface temperature and rainfall were used to investigate meteorological effects to air pollutants. Seasonal patterns were observed in the time series of NO2, O3 and CO alongside rainfall and LST. During the dry season, high LST and low precipitation is observed to be associated with increase in NO2, O3, and CO concentrations. On the other hand, wet seasons show decreases in concentrations of air pollutants, consistent with the washout effect. The NO2 average concentration in NCR is 1.9, 2.1, 2.3 times higher than in Metro Cebu, Davao City, and Legazpi City, respectively. In contrast, SO2 average concentration is highest in Legazpi City due to the nearby active volcano by a maximum factor of 1.8 compared to other cities. In addition, air quality changes brought about by community quarantines were examined since the onset of the COVID-19 crisis. Transition from the pre-quarantine period to the first lockdown shows sudden decrease by 28% in satellite-based retrievals of NO2 in NCR, mainly due to reduced anthropogenic emissions. As tiers of community quarantines were introduced, an increase in pollutant concentrations was observed, returning to pre-pandemic air quality as the guidelines ease physical and economic restrictions. Monitoring and analyzing the patterns in concentration of air pollutants in relation to meteorological and anthropogenic drivers can help in the air quality management in the country. © 43rd Asian Conference on Remote Sensing, ACRS 2022.

The Role of Traffic Volume on Sound Pressure Level Reduction before and during COVID-19 Lockdown Measures-A Case Study in Bochum, Germany.

During the SARS-CoV-2 pandemic, sound pressure levels (SPL) decreased because of lockdown measures all over the world. This study aims to describe SPL changes over varying lockdown measure timeframes and estimate the role of traffic on SPL variations. To account for different COVID-19 lockdown measures, the timeframe during the pandemic was segmented into four phases. To analyze the association between a-weighted decibels (dB(A)) and lockdown phases relative to the pre-lockdown timeframe, we calculated a linear mixed model, using 36,710 h of recording time. Regression coefficients depicting SPL changes were compared, while the model was subsequently adjusted for wind speed, rainfall, and traffic volume. The relative adjusted reduction of during pandemic phases to pre-pandemic levels ranged from -0.99 dB(A) (CI: -1.45; -0.53) to -0.25 dB(A) (CI: -0.96; 0.46). After controlling for traffic volume, we observed little to no reduction (-0.16 dB(A) (CI: -0.77; 0.45)) and even an increase of 0.75 dB(A) (CI: 0.18; 1.31) during the different lockdown phases. These results showcase the major role of traffic regarding the observed reduction. The findings can be useful in assessing measures to decrease noise pollution for necessary future population-based prevention.

Insight into vaccination and meteorological factors on daily COVID-19 cases and mortality in Bangladesh.

The ongoing COVID-19 contagious disease caused by SARS-CoV-2 has disrupted global public health, businesses, and economies due to widespread infection, with 676.41 million confirmed cases and 6.77 million deaths in 231 countries as of February 07, 2023. To control the rapid spread of SARS-CoV-2, it is crucial to determine the potential determinants such as meteorological factors and their roles. This study examines how COVID-19 cases and deaths changed over time while assessing meteorological characteristics that could impact these disparities from the onset of the pandemic. We used data spanning two years across all eight administrative divisions, this is the first of its kind--showing a connection between meteorological conditions, vaccination, and COVID-19 incidences in Bangladesh. We further employed several techniques including Simple Exponential Smoothing (SES), Auto-Regressive Integrated Moving Average (ARIMA), Auto-Regressive Integrated Moving Average with explanatory variables (ARIMAX), and Automatic forecasting time-series model (Prophet). We further analyzed the effects of COVID-19 vaccination on daily cases and deaths. Data on COVID-19 cases collected include eight administrative divisions of Bangladesh spanning March 8, 2020, to January 31, 2023, from available online servers. The meteorological data include rainfall (mm), relative humidity (%), average temperature (°C), surface pressure (kPa), dew point (°C), and maximum wind speed (m/s). The observed wind speed and surface pressure show a significant negative impact on COVID-19 cases (-0.89, 95% confidence interval (CI): 1.62 to -0.21) and (-1.31, 95%CI: 2.32 to -0.29), respectively. Similarly, the observed wind speed and surface pressure show a significant negative impact on COVID-19 deaths (-0.87, 95% CI: 1.54 to -0.21) and (-3.11, 95%CI: 4.44 to -1.25), respectively. The impact of meteorological factors is almost similar when vaccination information is included in the model. However, the impact of vaccination in both cases and deaths model is significantly negative (for cases: 1.19, 95%CI: 2.35 to -0.38 and for deaths: 1.55, 95%CI: 2.88 to -0.43). Accordingly, vaccination effectively reduces the number of new COVID-19 cases and fatalities in Bangladesh. Thus, these results could assist future researchers and policymakers in the assessment of pandemics, by making thorough efforts that account for COVID-19 vaccinations and meteorological conditions.

Determining the Relationship of Meteorological Factors and Severe Pediatric Respiratory Syncytial Virus (RSV) Infection in Central Peninsular Malaysia.

Respiratory syncytial virus (RSV) is the most common pathogen causing viral respiratory tract infections among younger children worldwide. The influence of meteorological factors on RSV seasonal activity is well-established for temperate countries; however, in subtropical countries such as Malaysia, relatively stable temperate climates do not clearly support this trend, and the available data are contradictory. Better understanding of meteorological factors and seasonality of RSV will allow effective strategic health management relating to RSV infection, particularly immunoprophylaxis of high-risk infants with palivizumab. Retrospectively, from 2017 to 2021, we examined the association between various meteorological factors (rainfall, rainy days, temperature, and relative humidity) and the incidence of RSV in children aged less than 12 years in Kuala Lumpur, Malaysia. RSV activity peaked in two periods (July to August and October to December), which was significantly correlated with the lowest rainfall (p < 0.007) and number of rainy days (p < 0.005). RSV prevalence was also positively associated with temperature (p < 0.006) and inversely associated with relative humidity (p < 0.006). Based on our findings, we recommend that immunoprophylaxis with palivizumab be administered in children aged less than 2 years where transmission of RSV is postulated to be the highest after the end of two monsoon seasons.

Climate Change and Agricultural Output: The Need for Policy

Climate change has been identified as a major challenge to African countries given the prevalence of poverty, low infrastructural development, and the dependence of African countries on agriculture. The yearly rainfall pattern in Africa had been stable for most parts of the year. However, given the increasing variability in the duration and the intensity of the rains, dry season, the erratic and changing nature of weather systems like floods and extended periods of no rainfall affect farmers who rely on rainfall for their agricultural activity. Large-scale destruction of farmlands and villages by floods in Nigeria, Mali, Burkina Faso as well as many other countries in sub-Saharan Africa can be blamed for the dwindling food supply in the region. In an attempt to meet up with the food challenge, more virgin forests are being exploited leading to increased Green House Gases (GHGs) emissions. As a result, agriculture will certainly be affected as well as being a significant cause or major contributor to the incidence of climate change. Efforts had been put on rural development by African governments to reverse the effect of challenges posed by climate change. However, this had been limited by a sustained effect of worsening socioeconomic challenges, like the incidence of HIV-AIDS, COVID-19, other health challenges, food crises, hunger, and malnutrition. This scenario can be reduced with an effective adaptation strategy to climate change followed by a sound agricultural policy that will lead to the expansion of different channels of food access and an increase in food production. There is evidence that most of the small-holder African farmers are slow in adapting to variations in weather patterns resulting from climate change which affects the agricultural output. This calls for a comprehensive policy option that will turn the fortunes of the farmers towards improved agricultural productivity leading to increased access to food. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

How environmental factors affecting COVID-19 transmission? case study: Riyadh region in Saudi Arabia

In this study, I have conducted non-medical, non-clinical-care research that will enable immediate exploring of how environmental factors affect spread of COVID-19 in Kingdom of Saudi Arabia (KSA). It focusses on climatic environmental factors that affect the distribution and population size of disease vectors and the relationship(s) between each of these environmental variables that provided from National Center for Metrology and COVID-19 infected cases from Ministry of Health in KSA. I used daily environmental data, including minimum, maximum, and averages temperatures (°C), rainfall amounts (mm), wind speed (KTS/Deg) and relative humidity (%) over the Riyadh region in Saudi Arabia. Spearman's rank correlation coefficient used to analyze the data. The results showed that average temperatures, minimum temperatures, and maximum temperatures were significantly correlated with a COVID-19 epidemic, (r = 0.527;0.509;0.530 respectively). A negative correlation was found with relative humidity (r = -0.475). These findings will be used as lessons learned as well as best practices in the future to help decision makers to understand the factors controlling COVID-19′s spread in KSA. © 2022 The Author(s)

Lockdown as an Empirical Strategy to Curb Air Pollution: Evidence from a Quasi‐Natural Experiment

In this study, three approaches namely parallel, sequential, and multiple linear regression are applied to analyze the local air quality improvements during the COVID‐19 lockdowns. In the present work, the authors have analyzed the monitoring data of the following primary air pollutants: particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). During the lockdown period, the first phase has most noticeable impact on airquality evidenced by the parallel approach, and it has reflected a significant reduction in concentration levels of PM10 (27%), PM2.5 (19%), NO2 (74%), SO2 (36%), and CO (47%), respectively. In the sequential approach, a reduction in pollution levels is also observed for different pollutants, however, these results are biased due to rainfall in that period. In the multiple linear regression approach, the concentrations of primary air pollutants are selected, and set as target variables to predict their expected values during the city's lockdown period.The obtained results suggest that if a 21‐days lockdown is implemented, then a reduction of 42 µg m−3 in PM10, 23 µg m−3 in PM2.5, 14 µg m−3 in NO2, 2 µg m−3 in SO2, and 0.7 mg m−3 in CO can be achieved.

A Study on the Association Between Climate and Corona Virus Outspread in South Indian States

In this study, we objectively focus on the relationship between the number of coronavirus (COVID-19) cases and key climate variables. We found that the risk of COVID-19 was approximately doubled during warm summer months when the aerosol molecules are likely stimulated by temperature and rainfall. Given that India is currently emerging as the new epicenter for the third and fourth outbreaks of COVID-19, we selected four key hotspot states-Maharashtra, Andhra Pradesh, Kerala, and Tamil Nadu - to closely look into the impact of climate variables on the spread of COVID-19 infected cases during 2020 and 2021. We found that COVID-19 is most active in temperature between 27 degrees C and 32 degrees C, while it is active in monthly average rainfall between 250 mm and 350 mm. This study further confirms that, although temperature and rainfall are not the initial triggers of COVID-19, both variables seem to play significant roles in spreading COVID-19 in India, especially during the summer season of 2020 and 2021, when the Indian summer monsoon was stronger in these four states.

Climate Change and Agricultural Output: The Need for Policy

Climate change has been identified as a major challenge to African countries given the prevalence of poverty, low infrastructural development, and the dependence of African countries on agriculture. The yearly rainfall pattern in Africa had been stable for most parts of the year. However, given the increasing variability in the duration and the intensity of the rains, dry season, the erratic and changing nature of weather systems like floods and extended periods of no rainfall affect farmers who rely on rainfall for their agricultural activity. Large-scale destruction of farmlands and villages by floods in Nigeria, Mali, Burkina Faso as well as many other countries in sub-Saharan Africa can be blamed for the dwindling food supply in the region. In an attempt to meet up with the food challenge, more virgin forests are being exploited leading to increased Green House Gases (GHGs) emissions. As a result, agriculture will certainly be affected as well as being a significant cause or major contributor to the incidence of climate change. Efforts had been put on rural development by African governments to reverse the effect of challenges posed by climate change. However, this had been limited by a sustained effect of worsening socioeconomic challenges, like the incidence of HIV-AIDS, COVID-19, other health challenges, food crises, hunger, and malnutrition. This scenario can be reduced with an effective adaptation strategy to climate change followed by a sound agricultural policy that will lead to the expansion of different channels of food access and an increase in food production. There is evidence that most of the small-holder African farmers are slow in adapting to variations in weather patterns resulting from climate change which affects the agricultural output. This calls for a comprehensive policy option that will turn the fortunes of the farmers towards improved agricultural productivity leading to increased access to food. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Indirect Response of the Temperature, Humidity, and Rainfall on the Spread of COVID-19 over the Indian Monsoon Region.

This article examines the role of the meteorological variable in the spread of the ongoing pandemic coronavirus disease 2019 (COVID-19) across India. COVID-19 has created an unprecedented situation for public health and brought the world to a standstill. COVID-19 had caused more than 1,523,242 deaths out of 66,183,029 confirmed cases worldwide till the first week of December 2020. We have examined the surface temperature, relative humidity, and rainfall over five cities: Delhi, Mumbai, Kolkata, Bengaluru, and Chennai, which were severely affected by COVID-19. It is found that the prevailing southwest (SW) monsoon during the pandemic has acted as a natural sanitizer in limiting the spread of the virus. The mean rainfall is ~ 20-40 mm over the selected cities, resulting in an average decrease in COVID cases by ~ 18-26% for the next 3 days after the rainfall. The day-to-day variations of the meteorological parameters and COVID-19 cases clearly demonstrate that both surface temperature and relative humidity play a vital role in the indirect transport of the virus. Our analysis reveals that most COVID-19 cases fall within the surface temperature range from 24 to 30 °C and relative humidity range from 50% to 80%. At a given temperature, COVID-19 cases show a large dependency on the relative humidity; therefore, the coastal environments were more prone to infections. Wavelet transforms coherence analysis of the daily COVID-19 cases with temperature and relative humidity reveals a significant coherence within 8 days.

SPECIAL ISSUE ON THE JULY 2020 HEAVY RAINFALL EVENT IN JAPAN

In July 2020, the Kyushu region experienced record-breaking heavy rains from July 4-7, causing extreme floods in the Kuma and Chikugo Rivers. This was followed by atmospheric instability over a wide area from western Japan to the Tohoku region, resulting in heavy rainfall on July 13-14 in the Chugoku region, and on July 27-28 in the Tohoku region, and flooding of large rivers including the Go River and the Mogami River. In recent years, record-breaking torrential rainfall disasters have been occurring every year, and the heavy precipitation scale as well as the rainfall intensity has been increasing in space and time, resulting in spatio-temporal expansion of the damage. Furthermore, in 2020, the disaster occurred while the social activities had been restricted due to COVID-19 pandemic. The compilation and dissemination of disaster survey data and lessons are essential toward the sustainable development of society. Therefore, JSCE has planned a special issue on the July 2020 torrential rain disaster in order to share and disseminate disaster information and to contribute to the advancement of technology and science related to disaster prevention and mitigation.抄録　令和2年7月，九州地方では4日から7日にかけて記録的な大雨となり，球磨川や筑後川では記録的な洪水が発生した．その後も西日本から東北地方の広い範囲で大気が不安定となり，江の川，最上川など大河川においても氾濫が相次いだ．近年では，毎年のように記録的な豪雨災害が発生しており，降雨強度だけでなく降雨のスケールが時空間的に大きくなっており，被害が時空間的に拡大している．さらに，令和2年度は新型コロナウィルスによる感染症対策のため，人々の活動が制限される中での災害となった．災害調査データを取り纏め，情報発信することは今後の持続可能な社会を検討する上で不可欠である．土木学会論文集では，災害情報を共有・発信し，防災に関する技術および学術分野の進展に資するために令和2年7月豪雨災害に関する特集を企画した．

FLOOD CONTROL MEASURES OF CORE MEDICAL INSTITUTION: LESSONS LEARNED FROM FLOODS IN KUMA RIVER IN 2020

Recordable heavy rainfall hit and caused severe floods from the Kuma River in Hitoyoshi and Kuma regions in July, 2020 due to an active frontal rain system. This paper aims to clarify challenges and issues in measures taken by core medical institutions against large-scale floods, which have been identified in the flood event in Kuma River in July 2020, based on the interview with the core medical institution in those regions. The paper then discusses directions for effective flood countermeasures of core medical institutions in order to have more robust Business Continuity Plan (BCP) for large-scale flood disasters.抄録　令和2年7月豪雨災害では，九州地方や東北地方などで大規模な出水となり，中でも九州地方では人的被害を含む甚大な被害が生じた．一方で，本災害は，新型コロナウイルス感染症（COVID-19）が全国的に流行する中で発生した最初の大規模水害であり，特殊な感染症への対応を継続しつつも，大規模な水害が発生した場合に地域の医療機能をどのように維持していくかが大きく問われた災害でもあった．本稿では，人吉・球磨地方における地域医療の拠点病院に対して令和2年7月豪雨災害での球磨川の氾濫に伴う浸水時の状況と対応についてヒアリングを行った結果を報告するとともに，これを踏まえた拠点医療機関の水害対策の課題と方向性について述べる．

Production and income dynamics of maize farmers in dryland of North Lombok Indonesia before and during the Covid-19 pandemic

The Covid-19 pandemic has had a broad impact on several aspects of human life, one of which is the issue of food sufficiency. Due to social restrictions, the agricultural sector, which plays a role in producing food for humans, may be affected by the pandemic. These restrictions impact the availability of farm labor and the market, both agricultural inputs, and outputs. This three-year study examines the factors that influence the productivity and income of maize farmers in the dry sandy lands of Gumantar village, North Lombok, Indonesia, before and during the Covid-19 pandemic. The method used was descriptive quantitative with 50 respondents, determined by accidental sampling. The study results showed variations in land area ownership of respondent farmers, variations in crop productivity, and variations in the form of products sold, such as selling cobs and selling grains. Maize production was more affected by rainfall, fertilizer availability, and pest disturbances than the Covid-19 pandemic. However, the Covid-19 pandemic impacted the income of maize farmers due to restrictions on the mobility of maize buyers.

How Environmental Factors Affecting COVID-19 Transmission? Case Study: Riyadh region in Saudi Arabia

In this study, I have conducted non-medical, non-clinical-care research that will enable immediate exploring of how environmental factors affect spread of COVID-19 in Kingdom of Saudi Arabia (KSA). It focusses on climatic environmental factors that affect the distribution and population size of disease vectors and the relationship(s) between each of these environmental variables that provided from National Center for Metrology and COVID-19 infected cases from Ministry of Health in KSA. I used daily environmental data, including minimum, maximum, and averages temperatures (°C), rainfall amounts (mm), wind speed (KTS/Deg) and relative humidity (%) over the Riyadh region in Saudi Arabia. Spearman's rank correlation coefficient used to analyze the data. The results showed that average temperatures, minimum temperatures, and maximum temperatures were significantly correlated with a COVID-19 epidemic, (r = 0.527;0.509;0.530 respectively). A negative correlation was found with relative humidity (r= -0.475). These findings will be used as lessons learned as well as best practices in the future to help decision makers to understand the factors controlling COVID-19's spread in KSA.