Publications -- Ubydul Haque

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12503/31220

This collection is limited to articles published under the terms of a creative commons license or other open access publishing agreement since 2016. It is not intended as a complete list of the author's works.

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Moderation effects of serotype on dengue severity across pregnancy status in Mexico
(BioMed Central Ltd., 2023-03-11) Annan, Esther; Nguyen, Uyen-Sa D. T.; Trevino, Jesus; Wan Yaacob, Wan F.; Mangla, Sherry; Pathak, Ashok K.; Nandy, Rajesh; Haque, Ubydul
BACKGROUND: Pregnancy increases a woman's risk of severe dengue. To the best of our knowledge, the moderation effect of the dengue serotype among pregnant women has not been studied in Mexico. This study explores how pregnancy interacted with the dengue serotype from 2012 to 2020 in Mexico. METHOD: Information from 2469 notifying health units in Mexican municipalities was used for this cross-sectional analysis. Multiple logistic regression with interaction effects was chosen as the final model and sensitivity analysis was done to assess potential exposure misclassification of pregnancy status. RESULTS: Pregnant women were found to have higher odds of severe dengue [1.50 (95% CI 1.41, 1.59)]. The odds of dengue severity varied for pregnant women with DENV-1 [1.45, (95% CI 1.21, 1.74)], DENV-2 [1.33, (95% CI 1.18, 1.53)] and DENV-4 [3.78, (95% CI 1.14, 12.59)]. While the odds of severe dengue were generally higher for pregnant women compared with non-pregnant women with DENV-1 and DENV-2, the odds of disease severity were much higher for those infected with the DENV-4 serotype. CONCLUSION: The effect of pregnancy on severe dengue is moderated by the dengue serotype. Future studies on genetic diversification may potentially elucidate this serotype-specific effect among pregnant women in Mexico.
Spatio-temporal dynamics of three diseases caused by Aedes-borne arboviruses in Mexico
(Springer Nature, 2022-11-02) Dong, Bo; Khan, Latifur; Smith, Madison; Trevino, Jesus; Zhao, Bingxin; Hamer, Gabriel L.; Lopez-Lemus, Uriel A.; Molina, Aracely Angulo; Lubinda, Jailos; Nguyen, Uyen-Sa D. T.; Haque, Ubydul
BACKGROUND: The intensity of transmission of Aedes-borne viruses is heterogeneous, and multiple factors can contribute to variation at small spatial scales. Illuminating drivers of heterogeneity in prevalence over time and space would provide information for public health authorities. The objective of this study is to detect the spatiotemporal clusters and determine the risk factors of three major Aedes-borne diseases, Chikungunya virus (CHIKV), Dengue virus (DENV), and Zika virus (ZIKV) clusters in Mexico. METHODS: We present an integrated analysis of Aedes-borne diseases (ABDs), the local climate, and the socio-demographic profiles of 2469 municipalities in Mexico. We used SaTScan to detect spatial clusters and utilize the Pearson correlation coefficient, Randomized Dependence Coefficient, and SHapley Additive exPlanations to analyze the influence of socio-demographic and climatic factors on the prevalence of ABDs. We also compare six machine learning techniques, including XGBoost, decision tree, Support Vector Machine with Radial Basis Function kernel, K nearest neighbors, random forest, and neural network to predict risk factors of ABDs clusters. RESULTS: DENV is the most prevalent of the three diseases throughout Mexico, with nearly 60.6% of the municipalities reported having DENV cases. For some spatiotemporal clusters, the influence of socio-economic attributes is larger than the influence of climate attributes for predicting the prevalence of ABDs. XGBoost performs the best in terms of precision-measure for ABDs prevalence. CONCLUSIONS: Both socio-demographic and climatic factors influence ABDs transmission in different regions of Mexico. Future studies should build predictive models supporting early warning systems to anticipate the time and location of ABDs outbreaks and determine the stand-alone influence of individual risk factors and establish causal mechanisms.
The human toll and humanitarian crisis of the Russia-Ukraine war: the first 162 days
(BMJ Publishing Group Ltd., 2022-09-28) Haque, Ubydul; Naeem, Amna; Wang, Shanshan; Espinoza, Juan; Holovanova, Irina; Gutor, Taras; Bazyka, Dimitry; Galindo, Rebeca; Sharma, Sadikshya; Kaidashev, Igor P.; Chumachenko, Dmytro; Linnikov, Syvatoslav; Annan, Esther; Lubinda, Jailos; Korol, Natalya; Bazyka, Kostyantyn; Zhyvotovska, Liliia; Zimenkovsky, Andriy; Nguyen, Uyen-Sa D.T.
BACKGROUND: We examined the human toll and subsequent humanitarian crisis resulting from the Russian invasion of Ukraine, which began on 24 February 2022. METHOD: We extracted and analysed data resulting from Russian military attacks on Ukrainians between 24 February and 4 August 2022. The data tracked direct deaths and injuries, damage to healthcare infrastructure and the impact on health, the destruction of residences, infrastructure, communication systems, and utility services - all of which disrupted the lives of Ukrainians. RESULTS: As of 4 August 2022, 5552 civilians were killed outright and 8513 injured in Ukraine as a result of Russian attacks. Local officials estimate as many as 24 328 people were also killed in mass atrocities, with Mariupol being the largest (n=22 000) such example. Aside from wide swaths of homes, schools, roads, and bridges destroyed, hospitals and health facilities from 21 cities across Ukraine came under attack. The disruption to water, gas, electricity, and internet services also extended to affect supplies of medications and other supplies owing to destroyed facilities or production that ceased due to the war. The data also show that Ukraine saw an increase in cases of HIV/AIDS, tuberculosis, and Coronavirus (COVID-19). CONCLUSIONS: The 2022 Russia-Ukraine War not only resulted in deaths and injuries but also impacted the lives and safety of Ukrainians through destruction of healthcare facilities and disrupted delivery of healthcare and supplies. The war is an ongoing humanitarian crisis given the continuing destruction of infrastructure and services that directly impact the well-being of human lives. The devastation, trauma and human cost of war will impact generations of Ukrainians to come.
Social Media Use and Mental Health: A Global Analysis
(MDPI, 2022-11-24) Ulvi, Osman; Karamehic-Muratovic, Ajlina; Baghbanzadeh, Mahdi; Bashir, Ateka; Smith, Jacob; Haque, Ubydul
Research indicates that excessive use of social media can be related to depression and anxiety. This study conducted a systematic review of social media and mental health, focusing on Facebook, Twitter, and Instagram. Based on inclusion criteria from the systematic review, a meta-analysis was conducted to explore and summarize studies from the empirical literature on the relationship between social media and mental health. Using PRISMA guidelines on PubMed and Google Scholar, a literature search from January 2010 to June 2020 was conducted to identify studies addressing the relationship between social media sites and mental health. Of the 39 studies identified, 20 were included in the meta-analysis. Results indicate that while social media can create a sense of community for the user, excessive and increased use of social media, particularly among those who are vulnerable, is correlated with depression and other mental health disorders.
Determining Perceived Self-Efficacy for Preventing Dengue Fever in Two Climatically Diverse Mexican States: A Cross-Sectional Study
(MDPI, 2022-03-28) Annan, Esther; Angulo-Molina, Aracely; Yaacob, Wan Fairos Wan; Kline, Nolan; Lopez-Lemus, Uriel A.; Haque, Ubydul
Knowledge of dengue fever and perceived self-efficacy toward dengue prevention does not necessarily translate to the uptake of mosquito control measures. Understanding how these factors (knowledge and self-efficacy) influence mosquito control measures in Mexico is limited. Our study sought to bridge this knowledge gap by assessing individual-level variables that affect the use of mosquito control measures. A cross-sectional survey with 623 participants was administered online in Mexico from April to July 2021. Multiple linear regression and multiple logistic regression models were used to explore factors that predicted mosquito control scale and odds of taking measures to control mosquitoes in the previous year, respectively. Self-efficacy (beta = 0.323, p-value = < 0.0001) and knowledge about dengue reduction scale (beta = 0.316, p-value =< 0.0001) were the most important predictors of mosquito control scale. The linear regression model explained 24.9% of the mosquito control scale variance. Increasing age (OR = 1.064, p-value =< 0.0001) and self-efficacy (OR = 1.020, p-value = 0.0024) were both associated with an increase in the odds of taking measures against mosquitoes in the previous year. There is a potential to increase mosquito control awareness and practices through the increase in knowledge about mosquito reduction and self-efficacy in Mexico.
Spatial and temporal patterns of dengue incidence in northeastern Thailand 2006-2016
(BioMed Central Ltd., 2019-08-23) Phanitchat, Thipruethai; Zhao, Bingxin; Haque, Ubydul; Pientong, Chamsai; Ekalaksananan, Tipaya; Aromseree, Sirinart; Thaewnongiew, Kesorn; Fustec, Benedicte; Bangs, Michael J.; Alexander, Neal; Overgaard, Hans J.
BACKGROUND: Dengue, a viral disease transmitted by Aedes mosquitoes, is an important public health concern throughout Thailand. Climate variables are potential predictors of dengue transmission. Associations between climate variables and dengue have usually been performed on large-scale first-level national administrative divisions, i.e. provinces. Here we analyze data on a finer spatial resolution in one province, which is often more relevant for effective disease control design. The objective of this study was to investigate the effect of seasonal variations, monthly climate variability, and to identify local clusters of symptomatic disease at the sub-district level based on reported dengue cases. METHODS: Data on dengue cases were retrieved from the national communicable disease surveillance system in Thailand. Between 2006 and 2016, 15,167 cases were recorded in 199 sub-districts of Khon Kaen Province, northeastern Thailand. Descriptive analyses included demographic characteristics and temporal patterns of disease and climate variables. The association between monthly disease incidence and climate variations was analyzed at the sub-district level using Bayesian Poisson spatial regression. A hotspot analysis was used to assess the spatial patterns (clustered/dispersed/random) of dengue incidence. RESULTS: Dengue was predominant in the 5-14 year-old age group (51.1%). However, over time, dengue incidence in the older age groups (> 15 years) gradually increased and was the most affected group in 2013. Dengue outbreaks coincide with the rainy season. In the spatial regression model, maximum temperature was associated with higher incidence. The hotspot analysis showed clustering of cases around the urbanized area of Khon Kaen city and in rural areas in the southwestern portion of the province. CONCLUSIONS: There was an increase in the number of reported dengue cases in older age groups over the study period. Dengue incidence was highly seasonal and positively associated with maximum ambient temperature. However, climatic variables did not explain all the spatial variation of dengue in the province. Further analyses are needed to clarify the detailed effects of urbanization and other potential environmental risk factors. These results provide useful information for ongoing prediction modeling and developing of dengue early warning systems to guide vector control operations.
Development and Comparison of Dengue Vulnerability Indices Using GIS-Based Multi-Criteria Decision Analysis in Lao PDR and Thailand
(MDPI, 2021-09-06) Zafar, Sumaira; Shipin, Oleg; Paul, Richard E.; Rocklov, Joacim; Haque, Ubydul; Rahman, Md. Siddikur; Mayxay, Mayfong; Pientong, Chamsai; Aromseree, Sirinart; Poolphol, Petchaboon; Pongvongsa, Tiengkham; Vannavong, Nanthasane; Overgaard, Hans J.
Dengue is a continuous health burden in Laos and Thailand. We assessed and mapped dengue vulnerability in selected provinces of Laos and Thailand using multi-criteria decision approaches. An ecohealth framework was used to develop dengue vulnerability indices (DVIs) that explain links between population, social and physical environments, and health to identify exposure, susceptibility, and adaptive capacity indicators. Three DVIs were constructed using two objective approaches, Shannon's Entropy (SE) and the Water-Associated Disease Index (WADI), and one subjective approach, the Best-Worst Method (BWM). Each DVI was validated by correlating the index score with dengue incidence for each spatial unit (district and subdistrict) over time. A Pearson's correlation coefficient (r) larger than 0.5 and a p-value less than 0.05 implied a good spatial and temporal performance. Spatially, DVIWADI was significantly correlated on average in 19% (4-40%) of districts in Laos (mean r = 0.5) and 27% (15-53%) of subdistricts in Thailand (mean r = 0.85). The DVISE was validated in 22% (12-40%) of districts in Laos and in 13% (3-38%) of subdistricts in Thailand. The DVIBWM was only developed for Laos because of lack of data in Thailand and was significantly associated with dengue incidence on average in 14% (0-28%) of Lao districts. The DVIWADI indicated high vulnerability in urban centers and in areas with plantations and forests. In 2019, high DVIWADI values were observed in sparsely populated areas due to elevated exposure, possibly from changes in climate and land cover, including urbanization, plantations, and dam construction. Of the three indices, DVIWADI was the most suitable vulnerability index for the study area. The DVIWADI can also be applied to other water-associated diseases, such as Zika and chikungunya, to highlight priority areas for further investigation and as a tool for prevention and interventions.
Retrospective data analyses of social and environmental determinants of malaria control for elimination prospects in Eritrea
(BioMed Central Ltd., 2020-03-12) Mihreteab, Selam; Lubinda, Jailos; Zhao, Bingxin; Rodriguez-Morales, Alfonso J.; Karamehic-Muratovic, Ajlina; Goitom, Aman; Shad, Mohammad Yousaf; Haque, Ubydul
BACKGROUND: The present study focuses on both long- and short-term malaria transmission in Eritrea and investigates the risk factors. Annual aggregates of information on malaria cases, deaths, diagnostics and control interventions from 2001 to 2008 and monthly reported data from 2009 to 2017 were obtained from the National Malaria Control Programme. We used a generalized linear regression model to examine the associations among total malaria cases, death, insecticide-treated net coverage, indoor residual spraying and climatic parameters. RESULTS: Reduction in malaria mortality is demonstrated by the milestone margins of over 97% by the end of 2017. Malaria incidence likewise declined during the period (from 33 to 5 per 1000 population), representing a reduction of about 86% (R(2) = 0.3) slightly less than the decline in mortality. The distribution of insecticide treated nets generally declined between 2001 and 2014 (R(2) = 0.16) before increasing from 2015 to 2017, while the number of people protected by indoor residual spraying slightly increased (R(2) = 0.27). Higher rainfall was significantly associated with an increased number of malaria cases. The covariates rainfall and temperature are a better pair than IRS and LLIN to predict incidences. On the other hand, IRS and LLIN is a more significant pair to predict mortality cases. CONCLUSIONS: While Eritrea has made significant progress towards malaria elimination, this progress should be maintained and further improved. Distribution, coverage and utilization of malaria control and elimination tools should be optimized and sustained to safeguard the gains made. Additionally, consistent annual performance evaluation of malaria indicators would ensure a continuous learning process from gains/threats of epidemics and resurgence in regions already earmarked for elimination.
Dengue Seroprevalence and Seroconversion in Urban and Rural Populations in Northeastern Thailand and Southern Laos
(MDPI, 2020-12-07) Doum, Dyna; Overgaard, Hans J.; Mayxay, Mayfong; Suttiprapa, Sutas; Saichua, Prasert; Ekalaksananan, Tipaya; Tongchai, Panwad; Rahman, Md. Siddikur; Haque, Ubydul; Phommachanh, Sysavanh; Pongvongsa, Tiengkham; Rocklov, Joacim; Paul, Richard; Pientong, Chamsai
Dengue is the most rapidly spreading mosquito-borne viral disease in the world. The detection of clinical cases enables us to measure the incidence of dengue infection, whereas serological surveys give insights into the prevalence of infection. This study aimed to determine dengue seroprevalence and seroconversion rates in northeastern Thailand and southern Laos and to assess any association of mosquito control methods and socioeconomic factors with dengue virus (DENV) infection. Cross-sectional seroprevalence surveys were performed in May and November 2019 on the same individuals. Blood samples were collected from one adult and one child, when possible, in each of 720 randomly selected households from two urban and two rural sites in both northeastern Thailand and southern Laos. IgG antibodies against DENV were detected in serum using a commercial enzyme-linked immunosorbent assay (ELISA) kit. Overall, 1071 individuals participated in the study. The seroprevalence rate was high (91.5%) across all 8 study sites. Only age and province were associated with seroprevalence rates. There were 33 seroconversions during the period from May to November, of which seven reported fever. More than half of the seroconversions occurred in the rural areas and in Laos. Dengue seroconversion was significantly associated with young age (<15 years old), female gender, province, and duration of living in the current residence. No socioeconomic factors or mosquito control methods were found to be associated with seroprevalence or seroconversion. Notably, however, the province with most seroconversions had lower diurnal temperature ranges than elsewhere. In conclusion, our study has highlighted the homogeneity of dengue exposure across a wide range of settings and most notably those from rural and urban areas. Dengue can no longer be considered to be solely an urban disease nor necessarily one linked to poverty.
Environmental suitability for Aedes aegypti and Aedes albopictus and the spatial distribution of major arboviral infections in Mexico
(Elsevier Ltd., 2019-08-12) Lubinda, Jailos; Trevino, C. Jesus A.; Walsh, Mallory Rose; Moore, Adrian J.; Hanafi-Bojd, Ahmad Ali; Akgun, Seval; Zhao, Bingxin; Barro, Alassane S.; Begum, Mst Marium; Jamal, Hera; Angulo-Molina, Aracely; Haque, Ubydul
BACKGROUND: This paper discusses a comparative geographic distribution of Aedes aegypti and Aedes albopictus mosquitoes in Mexico, using environmental suitability modeling and reported cases of arboviral infections. METHODS: Using presence-only records, we modeled mosquito niches to show how much they influenced the distribution of Ae. aegypti and Ae. albopictus based on mosquito records collected at the municipality level. Mosquito surveillance data were used to create models regarding the predicted suitability of Ae. albopictus and Ae. aegypti mosquitos in Mexico. RESULTS: Ae. albopictus had relatively a better predictive performance (area under the curve, AUC=0.87) to selected bioclimatic variables compared to Ae. aegypti (AUC=0.81). Ae. aegypti were more suitable for areas with minimum temperature of coldest month (Bio6, permutation importance 28.7%) -6 degrees C to 21.5 degrees C, cumulative winter growing degree days (GDD) between 40 and 500, and precipitation of wettest month (Bio13) >8.4mm. Minimum temperature range of the coldest month (Bio6) was -6.6 degrees C to 20.5 degrees C, and average precipitation of the wettest month (Bio13) 8.9mm~600mm were more suitable for the existence of Ae. albopictus. However, arboviral infections maps prepared from the 2012-2016 surveillance data showed cases were reported far beyond predicted municipalities. CONCLUSIONS: This study identified the urgent necessity to start surveillance in 925 additional municipalities that reported arbovirus infections but did not report Aedes mosquito.
Impact of Environmental Indicators on the COVID-19 Pandemic in Delhi, India
(MDPI, 2021-08-09) Mangla, Sherry; Pathak, Ashok Kumar; Arshad, Mohd; Ghosh, Doyel; Sahoo, Prafulla Kumar; Garg, Vinod Kumar; Haque, Ubydul
Currently, there is a massive debate on whether meteorological and air quality parameters play a crucial role in the transmission of COVID-19 across the globe. With this background, this study aims to evaluate the impact of air pollutants (PM2.5, PM10, CO, NO, NO2, and O3) and meteorological parameters (temperature, humidity, wind speed, and rainfall) on the spread and mortality due to the COVID-19 outbreak in Delhi from 14 Mar 2020 to 3 May 2021. The Spearman's rank correlation method employed on secondary data shows a significant correlation between the COVID-19 incidences and the PM2.5, PM10, CO, NO, NO2, and O3 concentrations. Amongst the four meteorological parameters, temperature is strongly correlated with COVID-19 infections and deaths during the three phases, i.e., pre-lockdown (14 March 2020 to 24 March 2020) (r = 0.79), lockdown (25 March 2020 to 31 May 2020) (r = 0.87), and unlock (1 June 2020 to 3 May 2021) (r = -0.75), explaining the variability of about 20-30% in the lockdown period and 18-19% in the unlock period. NO2 explained the maximum variability of 10% and 7% in the total confirmed cases and deaths among the air pollutants, respectively. A generalized linear model could explain 80% and 71% of the variability in confirmed cases and deaths during the lockdown and 82% and 81% variability in the unlock phase, respectively. These findings suggest that these factors may contribute to the transmission of the COVID-19 and its associated deaths. The study results would enhance the ongoing research related to the influence of environmental factors. They would be helpful for policymakers in managing the outbreak of COVID-19 in Delhi, India.
Short-term forecasting of the COVID-19 outbreak in India
(Oxford University Press, 2021-06-05) Mangla, Sherry; Pathak, Ashok Kumar; Arshad, Mohd; Haque, Ubydul
As the outbreak of coronavirus disease 2019 (COVID-19) is rapidly spreading in different parts of India, a reliable forecast for the cumulative confirmed cases and the number of deaths can be helpful for policymakers in making the decisions for utilizing available resources in the country. Recently, various mathematical models have been used to predict the outbreak of COVID-19 worldwide and also in India. In this article we use exponential, logistic, Gompertz growth and autoregressive integrated moving average (ARIMA) models to predict the spread of COVID-19 in India after the announcement of various unlock phases. The mean absolute percentage error and root mean square error comparative measures were used to check the goodness-of-fit of the growth models and Akaike information criterion for ARIMA model selection. Using COVID-19 pandemic data up to 20 December 2020 from India and its five most affected states (Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu and Kerala), we report 15-days-ahead forecasts for cumulative confirmed cases and the number of deaths. Based on available data, we found that the ARIMA model is the best-fitting model for COVID-19 cases in India and its most affected states.
The Disproportionate Impact of COVID-19 among Undocumented Immigrants and Racial Minorities in the US
(MDPI, 2021-12-02) Hasan Bhuiyan, Mohammad Tawhidul; Mahmud Khan, Irtesam; Rahman Jony, Sheikh Saifur; Robinson, Renee; Nguyen, Uyen-Sa D.T.; Keellings, David; Rahman, M. Sohel; Haque, Ubydul
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19), has had an unprecedented effect, especially among under-resourced minority communities. Surveillance of those at high risk is critical for preventing and controlling the pandemic. We must better understand the relationships between COVID-19-related cases or deaths and characteristics in our most vulnerable population that put them at risk to target COVID-19 prevention and management efforts. Population characteristics strongly related to United States (US) county-level data on COVID-19 cases and deaths during all stages of the pandemic were identified from the onset of the epidemic and included county-level socio-demographic and comorbidities data, as well as daily meteorological modeled observation data from the North American Regional Reanalysis (NARR), and the NARR high spatial resolution model to assess the environment. Advanced machine learning (ML) approaches were used to identify outbreaks (geographic clusters of COVID-19) and included spatiotemporal risk factors and COVID-19 vaccination efforts, especially among vulnerable and underserved communities. COVID-19 outcomes were found to be negatively associated with the number of people vaccinated and positively associated with age, the prevalence of cardiovascular disease, diabetes, and the minority population. There was also a strong positive correlation between unauthorized immigrants and the prevalence of COVID-19 cases and deaths. Meteorological variables were also investigated, but correlations with COVID-19 were relatively weak. Our findings suggest that COVID-19 has had a disproportionate impact across the US population among vulnerable and minority communities. Findings also emphasize the importance of vaccinations and tailored public health initiatives (e.g., mask mandates, vaccination) to reduce the spread of COVID-19 and the number of COVID-19 related deaths across all populations.
Correction: Doum, D., et al. Dengue Seroprevalence and Seroconversion in Urban and Rural Populations in Northeastern Thailand and Southern Laos. Int. J. Environ. Res. Public Health 2020, 17, 9134
(MDPI, 2021-02-04) Doum, Dyna; Overgaard, Hans J.; Mayxay, Mayfong; Suttiprapa, Sutas; Saichua, Prasert; Ekalaksananan, Tipaya; Tongchai, Panwad; Rahman, Md. Siddikur; Haque, Ubydul; Phommachanh, Sysavanh; Pongvongsa, Tiengkham; Rocklov, Joacim; Paul, Richard; Pientong, Chamsai
There was an error in the original article [...].
Assessment of Knowledge, Attitudes, and Practices Regarding Dengue among Physicians: A Web-Based Cross-Sectional Survey
(MDPI, 2021-07-21) Koonisetty, Kranthi Swaroop; Aghamohammadi, Nasrin; Urmi, Tamanna; Yavasoglu, Sare Ilknur; Rahman, Md. Shahinur; Nandy, Rajesh; Haque, Ubydul
Dengue fever is one of the most important viral infections transmitted by Aedes mosquitoes and a major cause of morbidity and mortality globally. Accurate identification of cases and treatment of dengue patients at the early stages can reduce medical complications and dengue mortality rate. This survey aims to determine the knowledge, attitude, and practices (KAP) among physicians in dengue diagnosis and treatment. This study was conducted among physicians in Turkey as one nonendemic country and Bangladesh, India, and Malaysia as three dengue-endemic countries. The dosing frequencies, maximum doses, and contraindications in dengue fever were examined. The results found that physicians from Bangladesh, India, and Malaysia have higher KAP scores in dengue diagnosis and treatment compared to physicians in Turkey. This may be due to a lack of physician's exposure to a dengue patient as Turkey is considered a nonendemic country. This assessment may help establish a guideline for intervention strategies among physicians to have successful treatment outcomes and reduce dengue mortality.
The Constant Threat of Zoonotic and Vector-Borne Emerging Tropical Diseases: Living on the Edge
(Frontiers Media S.A., 2021-05-04) Rodriguez-Morales, Alfonso J.; Paniz-Mondolfi, Alberto E.; Faccini-Martinez, Alvaro A.; Henao-Martinez, Andres F.; Ruiz-Saenz, Julian; Martinez-Gutierrez, Marlen; Alvarado-Arnez, Lucia E.; Gomez-Marin, Jorge E.; Bueno-Mari, Ruben; Carrero, Yenddy; Villamil-Gomez, Wilmer E.; Bonilla-Aldana, D. Katterine; Haque, Ubydul; Ramirez, Juan D.; Navarro, Juan-Carlos; Lloveras, Susanna; Arteaga-Livias, Kovy; Casalone, Cristina; Maguina, Jorge L.; Escobedo, Angel A.; Hidalgo, Marylin; Bandeira, Antonio C.; Mattar, Salim; Cardona-Ospina, Jamie A.; Suarez, Jose A.
A Computational Modeling Study of COVID-19 in Bangladesh
(The American Society of Tropical Medicine and Hygiene, 2020-11-02) Khan, Irtesam Mahmud; Haque, Ubydul; Kaisar, Samiha; Rahman, Mohammad Sohel
The COVID-19 pandemic has spread globally. Only three cases in Bangladesh were reported on March 8, 2020. Here, we aim to predict the epidemic progression for 1 year under different scenarios in Bangladesh. We extracted the number of daily confirmed cases from March 8 to July 20, 2020. We considered the suspected-infected-removed (SIR) model and performed a maximum likelihood-based grid search to determine the removal rate (). The transmission was modeled as a stochastic random walk process, and sequential Monte Carlo simulation was run 100 times with bootstrap fits to infer the transmission rate (beta) and R t. According to the simulation, the (real) peak daily incidence of 3,600 would be followed by a steady decline, reaching below 1,000 in late January 2021. Thus, the model predicted that there would still be more than 300 cases/day even after a year. However, with proper interventions, a much steeper decline would be achieved following the peak. If we apply a combined (0.8beta, 1.2) intervention, there would be less than 100 cases by mid-October, only around five odd cases at the beginning of the year 2021, and zero cases in early March 2021. The predicted total number of deaths (in status quo) after 1 year would be 8,533 which would reduce to 3,577 if combined (0.8beta, 1.2) intervention is applied. We have also predicted the ideal number of tests that Bangladesh should perform and based on that redid the whole simulation. The outcome, though worse, would be manageable with interventions according to the simulation.
COVID-19 Vaccine Hesitancy and Emerging Variants: Evidence from Six Countries
(MDPI, 2021-10-28) Mangla, Sherry; Zohra Makkia, Fatima Tuz; Pathak, Ashok Kumar; Robinson, Renee; Sultana, Nargis; Koonisetty, Kranthi Swaroop; Karamehic-Muratovic, Ajlina; Nguyen, Uyen-Sa D.T.; Rodriguez-Morales, Alfonso J.; Sanchez-Duque, Jorge A.; Zamba, Patrick T.; Aghamohammadi, Nasrin; Cs, Fong; Haque, Ubydul
As the world tries to cope with the devastating effects of the COVID-19 pandemic and emerging variants of the virus, COVID-19 vaccination has become an even more critical tool toward normalcy. The effectiveness of the vaccination program and specifically vaccine uptake and coverage, however, is a function of an individual's knowledge and individual opinion about the disease and available vaccines. This study investigated the knowledge, attitudes, and resulting community practice(s) associated with the new COVID-19 variants and vaccines in Bangladesh, Colombia, India, Malaysia, Zimbabwe, and the USA. A cross-sectional web-based Knowledge, Attitudes, and Practices (KAP) survey was administered to respondents living in six different countries using a structured and multi-item questionnaire. Survey questions were translated into English, Spanish, and Malay to accommodate the local language in each country. Associations between KAP and a range of explanatory variables were assessed using univariate and multiple logistic regression. A total of 781 responses were included in the final analysis. The Knowledge score mean was 24 (out of 46), Attitude score 28.9 (out of 55), and Practice score 7.3 (out of 11). Almost 65% of the respondents reported being knowledgeable about COVID-19 variants and vaccination, 55% reported a positive attitude toward available COVID-19 vaccines, and 85% reported engaging in practices that supported COVID-19 vaccination. From the multiple logistic models, we found post-graduate education (AOR = 1.83, 95% CI: 1.23-2.74) and an age range 45-54 years (AOR = 5.81, 95% CI: 2.30-14.69) to be significantly associated with reported COVID-19 knowledge. In addition, positive Attitude scores were associated with respondents living in Zimbabwe (AOR = 4.49, 95% CI: 2.04-9.90) and positive Practice scores were found to be associated with people from India (AOR = 3.68, 95% CI: 1.15-11.74) and high school education (AOR = 2.16, 95% CI: 1.07-4.38). This study contributes to the identification of socio-demographic factors associated with poor knowledge, attitudes, and practices relating to COVID-19 variants and vaccines. It presents an opportunity for collaboration with diverse communities to address COVID-19 misinformation and common sources of vaccine hesitancy (i.e., knowledge, attitudes, and practices).
Acute Inflammatory Mediators in Young Adult Patients with COVID-19 in Mexico
(MDPI, 2021-08-20) Maldonado-Cabrera, Anahi; Angulo-Molina, Aracely; Haque, Ubydul; Velazquez, Carlos; Alvarez-Villasenor, Andrea S.; Santacruz-Gomez, Karla J.; Gallego-Hernandez, Ana L.
Young adults (18-40 years old) are an active population with high risk of infection and transmission of COVID-19. They are considered a low-risk population due to its low 1.0% case fatality rate (CFR). Despite their high clinical usefulness to prevent fatal cases, inflammatory and coagulation biomarkers studies are limited. For this reason, we performed a retrospective cohort study with COVID-19 patients in Hermosillo, Mexico, to assess inflammation, coagulopathy profile, and severity outcomes in young adults. We analyzed blood samples to determine the neutrophil/lymphocyte ratio (NLR), neutrophil/monocyte ratio (NMR), lymphocyte/monocyte ratio (LMR), platelet/lymphocyte ratio (PLR), and C-reactive protein (C-RP). We included epidemiological features and comorbidities, and compared them to the severity status. Only 359 COVID-19-confirmed young adults were included in the ambulatory (44.8%), hospitalized (42.9%), and death (12%) severity groups. Laboratory results showed an increase in NMR, LMR, and C-RP associated with the aggravated patients. Additionally, obesity, arterial hypertension, and type-2 diabetes mellitus (T2DM) were associated with the COVID-19 severity outcome. We found that 9.1% and 30.3% of young adults presented the novel COVID-19-associated coagulopathy (CAC) and the risk of CAC, respectively. These parameters can be considered independent biomarkers reflecting an enhanced inflammatory process related to the COVID-19 prognosis.
Clinical Symptoms of Arboviruses in Mexico
(MDPI, 2020-11-19) Ananth, Sushmitha; Shrestha, Nistha; Treviño C., Jesús A.; Nguyen, Uyen-Sa; Haque, Ubydul; Angulo-Molina, Aracely; Lopez-Lemus, Uriel A.; Lubinda, Jailos; Sharif, Rashed Md; Zaki, Rafdzah Ahmad; Sánchez Casas, Rosa María; Cervantes, Diana; Nandy, Rajesh
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.

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