Miftahuzzannat Amrin

Bio: Miftahuzzannat Amrin is an academic researcher from Begum Rokeya University. The author has contributed to research in topics: Statistics & Mean squared error. The author has an hindex of 2, co-authored 2 publications receiving 14 citations.

COVID-19 Epidemic in Bangladesh among Rural and Urban Residents: An Online Cross-Sectional Survey of Knowledge, Attitudes, and Practices

Abstract: As other nations around the world, Bangladesh is facing enormous challenges with the novel coronavirus (COVID-19) epidemic. To design a prevention and control strategy for this new infectious disease, it is essential to first understand people’s knowledge, attitudes, and practices (KAP) regarding COVID-19. This study sought to determine KAP among rural and urban residents as well as predictors of preventive practices associated with COVID-19 in Bangladesh. A social media-based (Facebook) cross-sectional survey was conducted to explore these variables among Bangladeshi adults. Of 1520 respondents who completed the questionnaire, low level of good or sufficient knowledge of COVID-19 (70.8%) and practices associated with COVID-19 (73.8%) were found. Despite the low level of knowledge and practices, respondents’ attitude (78.9%) towards COVID-19 was relatively high. Results suggest that compared to urban, rural residents are at a particularly high risk of COVID-19 because they were found to have significantly lower knowledge (p = 0.001) and practice levels (p = 0.002) than were urban residents. Multivariable logistic regression analysis identified gender, education, knowledge of COVID-19 transmission, signs and symptoms, and sources of information as factors significantly associated with preventive practices against COVID-19. Further attention and effort should be directed toward increasing both knowledge and practices targeting the general population in Bangladesh, particularly the rural and less educated residents. Findings from this study provide baseline data that can be used to promote integrated awareness of and effective health education programs about COVID-19 prevention and control strategies in Bangladesh, and similar COVID-19 endemic countries.

Climate change and dengue fever knowledge, attitudes and practices in Bangladesh: a social media-based cross-sectional survey.

Abstract: BACKGROUND Bangladesh experienced its worst dengue fever (DF) outbreak in 2019. This study investigated the knowledge, attitudes and practices (KAP) among university students in Bangladesh and significant factors associated with their prevention practices related to climate change and DF. METHODS A social media-based (Facebook) cross-sectional KAP survey was conducted and secondary data of reported DF cases in 2019 extracted. Logistic regression and spatial analysis were run to examine the data. RESULTS Of 1500 respondents, 76% believed that climate change can affect DF transmission. However, participants reported good climate change knowledge (76.7%), attitudes (87.9%) and practices (39.1%). The corresponding figures for DF were knowledge (47.9%), attitudes (80.3%) and practices (25.9%). Good knowledge and attitudes were significantly associated with good climate change adaptation or mitigation practices (p<0.05). Good knowledge, attitudes and previous DF experiences were also found to be significantly associated with good DF prevention practices (p<0.001). There was no significant positive correlation between climate change and DF KAP scores and the number of DF cases. CONCLUSIONS Findings from this study provide baseline data that can be used to promote educational campaigns and intervention programs focusing on climate change adaptation and mitigation and effective DF prevention strategies among various communities in Bangladesh and similar dengue-endemic countries.

Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh

Abstract: Accurate predictive time series modelling is important in public health planning and response during the emergence of a novel pandemic. Therefore, the aims of the study are three-fold: (a) to model the overall trend of COVID-19 confirmed cases and deaths in Bangladesh; (b) to generate a short-term forecast of 8 weeks of COVID-19 cases and deaths; (c) to compare the predictive accuracy of the Autoregressive Integrated Moving Average (ARIMA) and eXtreme Gradient Boosting (XGBoost) for precise modelling of non-linear features and seasonal trends of the time series. The data were collected from the onset of the epidemic in Bangladesh from the Directorate General of Health Service (DGHS) and Institute of Epidemiology, Disease Control and Research (IEDCR). The daily confirmed cases and deaths of COVID-19 of 633 days in Bangladesh were divided into several training and test sets. The ARIMA and XGBoost models were established using those training data, and the test sets were used to evaluate each model’s ability to forecast and finally averaged all the predictive performances to choose the best model. The predictive accuracy of the models was assessed using the mean absolute error (MAE), mean percentage error (MPE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The findings reveal the existence of a nonlinear trend and weekly seasonality in the dataset. The average error measures of the ARIMA model for both COVID-19 confirmed cases and deaths were lower than XGBoost model. Hence, in our study, the ARIMA model performed better than the XGBoost model in predicting COVID-19 confirmed cases and deaths in Bangladesh. The suggested prediction model might play a critical role in estimating the spread of a novel pandemic in Bangladesh and similar countries.

Social research 2.0: virtual snowball sampling method using Facebook

Abstract: Purpose - The aim of this paper is to present a sampling method using virtual networks to study "hard-to-reach" populations. In the ambit of social research, the use of new technologies is still questioned because the selection bias is an obstacle to carry on scientific research on the Internet. In this regard, the authors' hypothesis is that the use of social networking sites (Web 2.0) can be effective for the study of "hard-to-reach" populations. The main advantages of this technique are that it can expand the geographical scope and facilitates the identification of individuals with barriers to access. Therefore, the use of virtual networks in non-probabilistic samples can increase the sample size and its representativeness. Design/methodology/approach - To test this hypothesis, a virtual method was designed using Facebook to identify Argentinean immigrant entrepreneurs in Spain (214 cases). A characteristic of this population is that some individuals are administratively invisible in national statistics because they have double nationality (non-EU and EU). The use of virtual sampling was combined with an online questionnaire as a complementary tool for Web 2.0 research in behavioural sciences. Findings - The number of cases detected by Facebook and the virtual response rate is higher than traditional snowball technique. The explanation is that people increase their level of confidence because the researcher shows his personal information (Facebook's profile) and also participates in their groups of interest (Facebook's groups). Moreover, the online questionnaires administration allows the quality of the information to be controlled and avoids duplication of cases. Originality/value - The present article is the first that uses Facebook as an instrument to study immigrants. Therefore its adoption represents a great challenge in the social research field because there are many barriers of access and search. It also proposes a novel mix of traditional methodologies updated with the use of new virtual possibilities of studying hard to reach populations, especially in areas of social research where the contributions of these methods are less developed.

Willingness to vaccinate against COVID-19 among Bangladeshi adults: Understanding the strategies to optimize vaccination coverage.

Abstract: BACKGROUND: Although the approved COVID-19 vaccine has been shown to be safe and effective, mass vaccination in Bangladeshi people remains a challenge. As a vaccination effort, the study provided an empirical evidence on willingness to vaccinate by sociodemographic, clinical and regional differences in Bangladeshi adults. METHODS: This cross-sectional analysis from a household survey of 3646 adults aged 18 years or older was conducted in 8 districts of Bangladesh, from December 12, 2020, to January 7, 2021. Multinomial regression examined the impact of socio-demographic, clinical and healthcare-releated factors on hesitancy and reluctance of vaccination for COVID-19. RESULTS: Of the 3646 respondents (2212 men [60.7%]; mean [sd] age, 37.4 [13.9] years), 74.6% reported their willingness to vaccinate against COVID-19 when a safe and effective vaccine is available without a fee, while 8.5% were reluctant to vaccinate. With a minimum fee, 46.5% of the respondents showed intent to vaccinate. Among the respondents, 16.8% reported adequate adherence to health safety regulations, and 35.5% reported high confidence in the country's healthcare system. The COVID-19 vaccine refusal was significantly high in elderly, rural, semi-urban, and slum communities, farmers, day-laborers, homemakers, low-educated group, and those who had low confidence in the country's healthcare system. Also, the prevalence of vaccine hesitancy was high in the elderly population, low-educated group, day-laborers, people with chronic diseases, and people with low confidence in the country's healthcare system. CONCLUSION: A high prevalence of vaccine refusal and hesitancy was observed in rural people and slum dwellers in Bangladesh. The rural community and slum dwellers had a low literacy level, low adherence to health safety regulations and low confidence in healthcare system. The ongoing app-based registration for vaccination increased hesitancy and reluctancy in low-educated group. For rural, semi-urban, and slum people, outreach centers for vaccination can be established to ensure the vaccine's nearby availability and limit associated travel costs. In rural areas, community health workers, valued community-leaders, and non-governmental organizations can be utilized to motivate and educate people for vaccination against COVID-19. Further, emphasis should be given to the elderly and diseased people with tailored health messages and assurance from healthcare professionals. The media may play a responsible role with the vaccine education program and eliminate the social stigma about the vaccination. Finally, vaccination should be continued without a fee and thus Bangladesh's COVID vaccination program can become a model for other low and middle-income countries.

Clinical Symptoms of Arboviruses in Mexico

Abstract: Arboviruses such as Chikungunya (CHIKV), Dengue (DENV), and Zika virus (ZIKV) have emerged as a significant public health concern in Mexico. The existing literature lacks evidence regarding the dispersion of arboviruses, thereby limiting public health policy's ability to integrate the diagnosis, management, and prevention. This study seeks to reveal the clinical symptoms of CHIK, DENV, and ZIKV by age group, region, sex, and time across Mexico. The confirmed cases of CHIKV, DENV, and ZIKV were compiled from January 2012 to March 2020. Demographic characteristics analyzed significant clinical symptoms of confirmed cases. Multinomial logistic regression was used to assess the association between clinical symptoms and geographical regions. Females and individuals aged 15 and older had higher rates of reported significant symptoms across all three arboviruses. DENV showed a temporal variation of symptoms by regions 3 and 5, whereas ZIKV presented temporal variables in regions 2 and 4. This study revealed unique and overlapping symptoms between CHIKV, DENV, and ZIKV. However, the differentiation of CHIKV, DENV, and ZIKV is difficult, and diagnostic facilities are not available in rural areas. There is a need for adequately trained healthcare staff alongside well-equipped lab facilities, including hematological tests and imaging facilities.

COVID-19 Epidemic in Bangladesh among Rural and Urban Residents: An Online Cross-Sectional Survey of Knowledge, Attitudes, and Practices

Abstract: As other nations around the world, Bangladesh is facing enormous challenges with the novel coronavirus (COVID-19) epidemic. To design a prevention and control strategy for this new infectious disease, it is essential to first understand people’s knowledge, attitudes, and practices (KAP) regarding COVID-19. This study sought to determine KAP among rural and urban residents as well as predictors of preventive practices associated with COVID-19 in Bangladesh. A social media-based (Facebook) cross-sectional survey was conducted to explore these variables among Bangladeshi adults. Of 1520 respondents who completed the questionnaire, low level of good or sufficient knowledge of COVID-19 (70.8%) and practices associated with COVID-19 (73.8%) were found. Despite the low level of knowledge and practices, respondents’ attitude (78.9%) towards COVID-19 was relatively high. Results suggest that compared to urban, rural residents are at a particularly high risk of COVID-19 because they were found to have significantly lower knowledge (p = 0.001) and practice levels (p = 0.002) than were urban residents. Multivariable logistic regression analysis identified gender, education, knowledge of COVID-19 transmission, signs and symptoms, and sources of information as factors significantly associated with preventive practices against COVID-19. Further attention and effort should be directed toward increasing both knowledge and practices targeting the general population in Bangladesh, particularly the rural and less educated residents. Findings from this study provide baseline data that can be used to promote integrated awareness of and effective health education programs about COVID-19 prevention and control strategies in Bangladesh, and similar COVID-19 endemic countries.

Ecological, social and other environmental determinants of dengue vector abundance in urban and rural areas of Northeastern Thailand

Abstract: Aedes aegypti is the main vector of dengue globally. The variables that influence the abundance of dengue vectors are numerous and complex. This has generated a need to focus on areas at risk of disease transmission, the spatial-temporal distribution of vectors, and the factors that modulate vector abundance. To help guide and improve vector-control efforts, this study identified the ecological, social, and other environmental risk factors that affect the abundance of adult female and immature Ae. aegypti in households in urban and rural areas of northeastern Thailand. A one-year entomological study was conducted in four villages of northeastern Thailand between January and December 2019. Socio-demographic; self-reported prior dengue infections; housing conditions; durable asset ownership; water management; characteristics of water containers; knowledge, attitudes, and practices (KAP) regarding climate change and dengue; and climate data were collected. Household crowding index (HCI), premise condition index (PCI), socio-economic status (SES), and entomological indices (HI, CI, BI, and PI) were calculated. Negative binomial generalized linear models (GLMs) were fitted to identify the risk factors associated with the abundance of adult females and immature Ae. aegypti. Urban sites had higher entomological indices and numbers of adult Ae. aegypti mosquitoes than rural sites. Overall, participants' KAP about climate change and dengue were low in both settings. The fitted GLM showed that a higher abundance of adult female Ae. aegypti was significantly (p < 0.05) associated with many factors, such as a low education level of household respondents, crowded households, poor premise conditions, surrounding house density, bathrooms located indoors, unscreened windows, high numbers of wet containers, a lack of adult control, prior dengue infections, poor climate change adaptation, dengue, and vector-related practices. Many of the above were also significantly associated with a high abundance of immature mosquito stages. The GLM model also showed that maximum and mean temperature with four-and one-to-two weeks of lag were significant predictors (p < 0.05) of the abundance of adult and immature mosquitoes, respectively, in northeastern Thailand. The low KAP regarding climate change and dengue highlights the engagement needs for vector-borne disease prevention in this region. The identified risk factors are important for the critical first step toward developing routine Aedes surveillance and reliable early warning systems for effective dengue and other mosquito-borne disease prevention and control strategies at the household and community levels in this region and similar settings elsewhere.