This study aims to determine the impact of COVID-19 on the daily lives, agricultural working lives, and mental health of farmers in northern Thailand. This cross-sectional study was carried out in September and October 2021 by interviews. From the stratified random sampling, 2046 farmers responded. There were five sections on the interview form, including demographics, daily life, agricultural working life, stress, and depression experienced during the COVID-19 pandemic. The results show that COVID-19 negatively affected the daily lives of the farmers, making it worse by 61.2%. COVID-19 increased the cost of planting (57.4%) and the cost of agrochemicals and fertilizers (69.9%). It also decreased the prices of agricultural products (73.5%) as well as agricultural extensions (66.5%). The markets and logistics of agricultural products during the pandemic were more difficult than before it (72.8% and 65.1%, respectively). Half of the farmers (50.3%) had moderate stress, and the highest scores were for the loss of household income (mean ± SD = 3.92 ± 0.94) and increased household expenses (mean ± SD = 3.92 ± 0.98). With regard to depression, 19.6% of farmers had depressive symptoms, and the multivariate analysis shows that the mental health of farmers was associated with the changes in their daily and agricultural working lives, as well as with financial problems. The remarkable findings indicate that the farmers who had high and extremely high stress levels had a higher prevalence of depression than the farmers who had no stress (adj.OR = 10.10 and 22.45, respectively). Our results lead to the conclusion that the COVID-19 crisis had an impact on the daily lives, agricultural working lives, and mental health of farmers. The results of this study can be used to provide pertinent guidance, and they have implications for government and other relevant organizations in their COVID-19 efforts to improve agricultural systems and sustain the mental health of farmers.

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Impact of COVID-19 Pandemic on Daily Lives, Agricultural Working Lives, and Mental Health of Farmers in Northern Thailand

Background Researchers have found innovative ways of using mobile health (mHealth) technologies to prevent the spread of coronavirus disease 2019 (COVID-19). However, fewer studies have been done to determine their adoption and effectiveness. Objective This review summarises the published evidence on the effect of mHealth technologies on the adoption of COVID-19 preventive measures, prevention knowledge acquisition and risk perception as well as technology adoption features for COVID-19 prevention. Methods PubMed, IEEE and Google Scholar databases were searched for peer-reviewed literature from 1 January 2020 to 31 March 2022 for studies that evaluated the effect of mHealth technologies on COVID-19 preventive measures adoption, prevention knowledge acquisition and risk perception. Thirteen studies met the inclusion criteria and were included in this review. All the included studies were checked for quality using the mHealth evidence reporting and assessment (mERA) checklist. Results The review found out that the utilisation of mHealth interventions such as alert text messages, tracing apps and social media platforms was associated with adherence behaviour such as wearing masks, washing hands and using sanitisers, maintaining social distance and avoiding crowded places. The use of contact tracing was linked to low-risk perception as users considered themselves well informed about their status and less likely to pose transmission risks compared to non-users. Privacy and security issues, message personalisation and frequency, technical issues and trust concerns were identified as technology adoption features that influence the use of mHealth technologies for promoting COVID-19 prevention. Conclusion Utilisation of mHealth may be a feasible and effective way to prevent the spread of COVID-19. However, the small study samples and short study periods prevent generalisation of the findings and calls for larger, longitudinal studies that encompass diverse study settings.

The role of mobile health technologies in promoting COVID-19 prevention: A narrative review of intervention effectiveness and adoption

Since November 2019, most countries across the globe have suffered from the disastrous consequences of the Covid-19 pandemic which redefined every aspect of human life. Given the inevitable spread and transmission of the virus, it is critical to acknowledge the factors that catalyse transmission of the disease. This research investigates the relation of the external demographic parameters such as total population, population density and weighted population density on the spread of Covid-19 in Malaysia. Pearson correlation and simple linear regression were utilized to identify the relation between the population-related variables and the spread of Covid-19 in Malaysia using data from 15th March 2020 to 31st March 2021. As a result, a strong positive significant correlation between the total population and Covid-19 cases was found. However, a weak positive relationship was found between the density variable (population density and weighted population density) and the spread of Covid-19. Our findings suggest that the transmission of Covid-19 during lockdown (Movement Control Order, MCO) in Malaysia was more readily explained by the demographic variable population size, than population density or weighted population density. Thus, this study could be helpful in intervention planning and managing future virus outbreaks in Malaysia.

Effect of total population, population density and weighted population density on the spread of Covid-19 in Malaysia

This study compared the effectiveness of COVID-19 control policies, including wearing masks, and the vaccine rates through proportional infection rate in 28 states of the United States using the eSIR model. The effective rate of policies was measured by the difference between the predicted daily infection proportion rate using the data before the policy and the actual daily infection proportion rate. The study suggests that both mask and vaccine policy had a significant impact on mitigating the pandemic. We further explored how different social factors influenced the effectiveness of a specific policy through the linear regression model. Out of 9 factors, the population density, number of hospital beds per 1000 people, and percent of the population over 65 are the most substantial factors on mask policy effectiveness, while public health funding per person, percent of immigration have the most significant influence on vaccine policy effectiveness. This study summarized the effectiveness of different policies and factors they associated with. It can be served as a reference for future covid-19 related policy.

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New Approach to Policy Effectiveness for Covid-19 and Factors Influence Policy Effectiveness

The COVID-19 pandemic has so far accounted for reported 5.5M deaths worldwide, with 8.7% of these coming from India. The pandemic exacerbated the weakness of the Indian healthcare system. As of January 20, 2022, India is the second worst affected country with 38.2M reported cases and 487K deaths. According to epidemiologists, vaccines are an essential tool to prevent the spread of the pandemic. India's vaccination drive began on January 16, 2021 with governmental policies being introduced to prioritize different populations of the society. Through the course of the vaccination drive, multiple new policies were also introduced to ensure that vaccines are readily available and vaccination coverage is increased. However, at the same time, some of the government policies introduced led to unintended inequities in the populations being targeted. In this report, we enumerate and analyze the inequities that existed in India's vaccination policy drive, and also compute the effect of the new policies that were introduced. We analyze these potential inequities not only qualitatively but also quantitatively by leveraging the data that was made available through the government portals. Specifically, (a) we discover inequities that might exist in the policies, (b) we quantify the effect of new policies introduced to increase vaccination coverage, and (c) we also point the data discrepancies that exist across different data sources.

Co-WIN: Really Winning? Analysing Inequity in India's Vaccination Response

This study compares the effectiveness of COVID-19 control policies on the virus’s spread and on the change of the infection dynamics in China, Germany, Austria, and the USA relying on a regression discontinuity in time and ‘earlyR’ epidemic models. The effectiveness of policies is measured by real-time reproduction number and cases counts. Comparison between the two lockdowns within each country showed the importance of people's risk perception for the effectiveness of the measures. Results suggest that restrictions applied for a long period or reintroduced later may cause at-tenuated effect on the circulation of the virus and the number of casualties.

Analyzing the Effectiveness of COVID-19 Lockdown Policies Using the Time-Dependent Reproduction Number and the Regression Discontinuity Framework: Comparison between Countries

Shangjun Liu

Papers

Analyzing the Effectiveness of COVID-19 Lockdown Policies Using the Time-Dependent Reproduction Number and the Regression Discontinuity Framework: Comparison between Countries