Application of Geospatial Technologies in the COVID-19 Fight of Ghana

Enhancing local-scale groundwater quality predictions using advanced machine learning approaches

Assessing groundwater quality typically involves labor-intensive, time-consuming, and costly laboratory tests, making real-time monitoring impractical, especially at the local level. Groundwater quality projections at the local scale using broad spatial datasets have been inaccurate due to variations in hydrogeology, human activities, industrial operations, groundwater extraction, and waste disposal. This study aims to identify the most dependable and resilient machine learning algorithms for forecasting groundwater quality at nearby monitoring locations by utilizing simple water quality metrics that can be quickly assessed without extensive sampling and laboratory testing. The Entropy-weighted Water Quality Index (EWQI) was calculated using a large spatial and temporal dataset (2014-2021) of 977 wells with parameters including pH, total hardness (TH), calcium (Ca2⁺), magnesium (Mg2⁺), sodium (Na⁺), potassium (K⁺), sulfate (SO₄2⁻), chloride (Cl⁻), nitrate (NO₃⁻), total dissolved solids (TDS), and fluoride (F⁻). Further, similar parameters were also observed in 33 open wells at the three local monitoring sites from December 2022 to March 2023. The EWQI was predicted using a Random Forest (RF), eXtreme Gradient Boosting (XGB), and Deep Neural Network (DNN). The pH, TH, and TDS were used as input variables for EWQI predictions, as they can be easily measured using handheld probes or multi-parameters. The model performance was evaluated using R2, MAE, and RMSE. During the training stage, all three models predicted the EWQI with an R2 greater than 90%, with minimal errors when pH, TH, and TDS were input variables. To validate the models at a local scale, the EWQI was predicted at the village level (e.g., Antoli, Balapura, and Lapodiaya) using pH, TH, and TDS as input variables. The machine learning models were able to predict the EWQI very closely to the actual EWQI, with an R2 greater than 90%. It is also evident that the models could predict the EWQI using basic parameters that are easily measured, providing an overall idea of the water quality for a small area. Hence, these machine learning models could be useful for accurately representing groundwater quality, thereby avoiding the use of time-consuming and costly laboratory techniques.

Appraising groundwater quality and probabilistic human health risks from fluoride-enriched groundwater using the pollution index of groundwater (PIG) and GIS: a case study of adama town and its vicinities in the central main Ethiopian rift valley

This research's main objective is to identify the level of contamination in drinking water in Adama town and its environs by employing PIG, GIS and HHRA. The physical-chemical parameters of groundwater were determined, and the results were compared to regional and global drinking water quality guidelines. The pH of groundwater is alkaline, and the contents of Ca2+, Na+, HCO3 -, and F- in the majority of samples surpassed the permissible drinking limit. The hydrochemical facies were identified in the following order: Ca-Mg-HCO3, Na-Ca-HCO3, and Na-HCO3. Cation exchange and Rock-water interaction are the major dominant natural mechanisms controlling groundwater chemistry. Using IDW interpolation methods with Arc GIS 10.8, spatial analysis of the physico-geochemical content of water divulged that TDS, pH, TH, EC, Mg2+, Ca2+, K+, Na+, Cl-, HCO3 -, F-, and SO4 2- all exhibit a positive trend in the direction of groundwater flow from the upland to the lowland (rift floor). As per PIG, the results show that 57%, 33%, 7% and 3% of the samples were found in the insignificant, low, moderate and high, correspondingly. The total hazard index (THI) is calculated from hazard quotients (HQIntake and HQDermal) results showing 83%, 73%, and 57% of the samples exceed the non-carcinogenic health threat of fluoride THI >1 in drinking water for children, women and men. Children are more susceptible to danger than either males or women, according to the THI data, based on body weights and consumption rates. Similarly, females are also more vulnerable to health risks than men.

COVID-19 pandemic and Fourth Industrial Revolution (4IR): Reflection on the challenges and opportunities in South Africa

Coronavirus disease 2019 (COVID-19) has changed people's interactions dramatically, be it with the government, the outside world, or one another. It has compelled society to change the standards for decision-making in areas such as health and education, and how people live, work, play, and do business. As a result of the new behavioral patterns imposed by governments around the world, including the South African government, there has been technological advancement through the Fourth Industrial Revolution (4IR). In South Africa, key economic sectors such as healthcare, education, and finance have served as digital change engines. Without a doubt, technology has been able to halt the spread of the virus. The main objective of this paper is to raise awareness of 4IR and to identify opportunities presented by 4IR for South Africa and the challenges presented by 4IR in resolving COVID-19 issues. It examines the relationship between evolving technological advances brought about by 4IR and COVID-19. Following a review of the relevant literature, the study's findings revealed that prior to the pandemic, several sectors were wary of, and slow to accept, 4IR. Despite the benefits of 4IR, many African countries face difficulties across various sectors. Almost all African countries are unprepared for 4IR, with the exception of South Africa, which is considered partially ready and ranks 22nd on the automation readiness index. African countries that have embraced digital transformation have cited South Africa as a case study due to its adoption of 4IR. Despite the benefits, several barriers to the adoption and use of 4IR technologies have been identified in developing countries, including South Africa, which has the opportunity to adopt consistent 4IR.

Role of academia in enhancing technology and innovation for a post COVID-19 recovery and growth

The COVID-19 pandemic represents one of the greatest challenges of this century with wide ranging impacts not only on health but practically every sector of the human society. The pandemic stretched our resources and coping capacities to almost breaking points even in wealthy economies and further exposed crucial weaknesses in infrastructure, human resources and emergency preparedness of most nations. This review article explored the role of technology and innovation in post COVID-19 growth and recovery. The academia has facilitated better understanding of the COVID-19 pandemic by increasing the body of knowledge on the disease. Better understanding of the disease informed technology and innovations which has made it possible to end the pandemic lockdown, and chart the course for recovery and growth. Relevant articles from a search of electronic databases were reviewed and the role of academia as well as some of the innovations that opened the pathway for recovery were highlighted. Recovery and growth after the COVID-19 pandemic will require synergistic efforts between the academia and the industry, more like taking the "Gown" to "Town". Research and development in academia, and industrial technology and innovation are veritable tools for a post COVID-19 recovery.

Africa's spatial data science landscape in the context of covid-19 pandemic

The emergence of Covid-19 pandemic in late 2019 presented daunting challenges for designing and implementing sustainable solutions at both local and global levels. The situation was dire in many developing economies with limited resources and vulnerable healthcare systems especially in Africa. Spatial data science (SDS) can be adopted and utilized to assist countries and local communities in understanding and effectively responding to Covid-19 pandemic. This article's study reviewed recent literature with the main goal to assess the application of this data-driven and technology-oriented modern approach in addressing Covid-19 in the African continent. Findings indicate that while examples of applications involving traditional geospatial technologies especially geographic information systems are abound, the use of more advanced SDS elements is limited and fragmented. Additionally, various studies leveraged SDS to address one or more complex questions against the backdrop of challenges largely influenced by the digital divide within Africa and across the globe. The article identifies and discusses these challenges as well as opportunities for increased use of SDS in Africa to understand and respond to disasters like Covid-19 and other complex problems. The argument is made for a more complete use of multiple elements of SDS.

Using space-time scan statistic for studying the effects of COVID-19 in Punjab, Pakistan: a guideline for policy measures in regional agriculture

Pakistan is included in top 50 countries which are estimated to face serious agriculture and food deficiency related challenges due to the worldwide pandemic coronavirus 2019 (COVID-19). The aim of this study was to evaluate the effects of COVID-19 on food supply chain and agriculture in Punjab, Pakistan, by using space-time scan statistic (STSS). A survey was conducted at 720 points in different districts of the province. The STSS detected "active" and emerging clusters that are current at the end of our study area-particularly, 17 clusters were formed while adding the updated case data. Software ArcGIS 10.3 was used to find relative risk (RR) values; the maximum RR value was found to be 42.19 and maximum observed cases 53,265 during June 15-July 1, 2020. It was not always necessary that if the number of active cases in Punjab increased, there should be higher relative risk for more number of districts and vice versa. Due to the highest number of cases of COVID-19 and RR values during July, mostly farmers faced many difficulties during the cultivation of cotton and rice. Mostly farmers (72%) observed increase in prices of inputs (fertilizers and pesticides) during lockdown. If the supply chain of agriculture related inputs is disturbed, farmers may find it quite difficult to access markets, which could result in a decline in production and sales of crops and livestock in study area. It is suggested that to protect the food security and to decrease the effect of the lockdown, Punjab government needs to review food policy and analyse how market forces will respond to the imbalanced storage facilities and capacity, supply and demand and price control of products. The findings of this study can also help policy-makers to formulate an effective food security and agriculture adaptation strategy.

Using drones to transport suspected COVID-19 samples; experiences from the second largest testing centre in Ghana, West Africa

BACKGROUND

The declaration of COVID-19 as a pandemic on March 11 2020, by the World Health Organisation prompted the need for a sustained and a rapid international response. In a swift response, the Government of Ghana, in partnership with Zipline company, launched the use of Unmanned Automated Vehicles (UAV) to transport suspected samples from selected districts to two foremost testing centres in the country. Here, we present the experiences of employing this technology and its impact on the transport time to the second largest testing centre, the Kumasi Centre for Collaborative Research in Tropical Medicine (KCCR) in Kumasi, Ghana.

METHODS

Swab samples collected from suspected COVID-19 patients were transported to the Zipline office by health workers. Information on the samples were sent to laboratory personnel located at KCCR through a WhatsApp platform to get them ready to receive the suspected COVID-19 samples while Zipline repackaged samples and transported them via drone. Time of take-off was reported as well as time of drop-off.

RESULTS

A total of 2537 COVID-19 suspected samples were received via drone transport from 10 districts between April 2020 to June 2021 in 440 deliveries. Ejura-Sekyedumase District Health Directorate delivered the highest number of samples (765; 30%). The farthest district to use the drone was Pru East, located 270 km away from KCCR in Kumasi and 173 km to the Zipline office in Mampong. Here, significantly, it took on the average 39 minutes for drones to deliver samples compared to 117 minutes spent in transporting samples by road (p<0.001).

CONCLUSION

The use of drones for sample transport during the COVID-19 pandemic significantly reduced the travel time taken for samples to be transported by road to the testing site. This has enhanced innovative measures to fight the pandemic using technology.

Epidemiological investigation of the COVID-19 outbreak in Vellore district in South India using Geographic Information Surveillance (GIS)

OBJECTIVES

Geographical Information Surveillance (GIS) is an advanced digital technology tool that maps location-based data and helps in epidemiological modeling. We applied GIS to analyze patterns of spread and hotspots of COVID-19 cases in the Vellore district in South India.

METHODS

Laboratory-confirmed COVID-19 cases from the Vellore district and neighboring taluks from March 2020 to June 2021 were geocoded and spatial maps were generated. Time trends exploring urban-rural burden with an age-sex distribution of cases and other variables were correlated with outcomes.

RESULTS

A total of 45,401 cases of COVID-19 were detected, with 20,730 cases during the first wave and 24,671 cases during the second wave. The overall incidence rates of COVID-19 were 462.8 and 588.6 per 100,000 population during the first and second waves, respectively. The spread pattern revealed epicenters in densely populated urban areas with radial spread sparing rural areas in the first wave. The case fatality rate was 1.89% and 1.6% during the first and second waves, which increased with advancing age.

CONCLUSIONS

Modern surveillance systems like GIS can accurately predict the trends and spread patterns during future pandemics. In addition, real-time mapping can help design risk mitigation strategies, thereby preventing the spread to rural areas.

Perspectives from remote sensing to investigate the COVID-19 pandemic: A future-oriented approach

As scientific technology and space science progress, remote sensing has emerged as an innovative solution to ease the challenges of the COVID-19 pandemic. To examine the research characteristics and growth trends in using remote sensing for monitoring and managing the COVID-19 research, a bibliometric analysis was conducted on the scientific documents appearing in the Scopus database. A total of 1,509 documents on this study topic were indexed between 2020 and 2022, covering 165 countries, 577 journals, 5239 institutions, and 8,616 authors. The studies related to remote sensing and COVID-19 have a significant increase of 30% with 464 articles. The United States (429 articles, 28.42% of the global output), China (295 articles, 19.54% of the global output), and the United Kingdom (174 articles, 11.53%) appeared as the top three most contributions to the literature related to remote sensing and COVID-19 research. Sustainability, Science of the Total Environment, and International Journal of Environmental Research and Public Health were the three most productive journals in this research field. The utmost predominant themes were COVID-19, remote sensing, spatial analysis, coronavirus, lockdown, and air pollution. The expansion of these topics appears to be associated with cross-sectional research on remote sensing, evidence-based tools, satellite mapping, and geographic information systems (GIS). Global pandemic risks will be monitored and managed much more effectively in the coming years with the use of remote sensing technology.

Evaluation of Vulnerability Status of the Infection Risk to COVID-19 Using Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA): A Case Study of Addis Ababa City, Ethiopia

COVID-19 is a disease caused by a new coronavirus called SARS-CoV-2 and is an accidental global public health threat. Because of this, WHO declared the COVID-19 outbreak a pandemic. The pandemic is spreading unprecedently in Addis Ababa, which results in extraordinary logistical and management challenges in response to the novel coronavirus in the city. Thus, management strategies and resource allocation need to be vulnerability-oriented. Though various studies have been carried out on COVID-19, only a few studies have been conducted on vulnerability from a geospatial/location-based perspective but at a wider spatial resolution. This puts the results of those studies under question while their findings are projected to the finer spatial resolution. To overcome such problems, the integration of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) has been developed as a framework to evaluate and map the susceptibility status of the infection risk to COVID-19. To achieve the objective of the study, data like land use, population density, and distance from roads, hospitals, bus stations, the bank, markets, COVID-19 cases, health care units, and government offices are used. The weighted overlay method was used; to evaluate and map the susceptibility status of the infection risk to COVID-19. The result revealed that out of the total study area, 32.62% (169.91 km2) falls under the low vulnerable category (1), and the area covering 40.9% (213.04 km2) under the moderate vulnerable class (2) for infection risk of COVID-19. The highly vulnerable category (3) covers an area of 25.31% (132.85 km2), and the remaining 1.17% (6.12 km2) is under an extremely high vulnerable class (4). Thus, these priority areas could address pandemic control mechanisms like disinfection regularly. Health sector professionals, local authorities, the scientific community, and the general public will benefit from the study as a tool to better understand pandemic transmission centers and identify areas where more protective measures and response actions are needed at a finer spatial resolution.

Leveraging Polio Geographic Information System Platforms in the African Region for Mitigating COVID-19 Contact Tracing and Surveillance Challenges: Viewpoint

BACKGROUND

The ongoing COVID-19 pandemic in Africa is an urgent public health crisis. Estimated models projected over 150,000 deaths and 4,600,000 hospitalizations in the first year of the disease in the absence of adequate interventions. Therefore, electronic contact tracing and surveillance have critical roles in decreasing COVID-19 transmission; yet, if not conducted properly, these methods can rapidly become a bottleneck for synchronized data collection, case detection, and case management. While the continent is currently reporting relatively low COVID-19 cases, digitized contact tracing mechanisms and surveillance reporting are necessary for standardizing real-time reporting of new chains of infection in order to quickly reverse growing trends and halt the pandemic.

OBJECTIVE

This paper aims to describe a COVID-19 contact tracing smartphone app that includes health facility surveillance with a real-time visualization platform. The app was developed by the AFRO (African Regional Office) GIS (geographic information system) Center, in collaboration with the World Health Organization (WHO) emergency preparedness and response team. The app was developed through the expertise and experience gained from numerous digital apps that had been developed for polio surveillance and immunization via the WHO's polio program in the African region.

METHODS

We repurposed the GIS infrastructures of the polio program and the database structure that relies on mobile data collection that is built on the Open Data Kit. We harnessed the technology for visualization of real-time COVID-19 data using dynamic dashboards built on Power BI, ArcGIS Online, and Tableau. The contact tracing app was developed with the pragmatic considerations of COVID-19 peculiarities. The app underwent testing by field surveillance colleagues to meet the requirements of linking contacts to cases and monitoring chains of transmission. The health facility surveillance app was developed from the knowledge and assessment of models of surveillance at the health facility level for other diseases of public health importance. The Integrated Supportive Supervision app was added as an appendage to the pre-existing paper-based surveillance form. These two mobile apps collected information on cases and contact tracing, alongside alert information on COVID-19 reports at the health facility level; the information was linked to visualization platforms in order to enable actionable insights.

RESULTS

The contact tracing app and platform were piloted between April and June 2020; they were then put to use in Zimbabwe, Benin, Cameroon, Uganda, Nigeria, and South Sudan, and their use has generated some palpable successes with respect to COVID-19 surveillance. However, the COVID-19 health facility-based surveillance app has been used more extensively, as it has been used in 27 countries in the region.

CONCLUSIONS

In light of the above information, this paper was written to give an overview of the app and visualization platform development, app and platform deployment, ease of replicability, and preliminary outcome evaluation of their use in the field. From a regional perspective, integration of contact tracing and surveillance data into one platform provides the AFRO with a more accurate method of monitoring countries' efforts in their response to COVID-19, while guiding public health decisions and the assessment of risk of COVID-19.

The basic reproduction number of COVID-19 across Africa

The pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) took the world by surprise. Following the first outbreak of COVID-19 in December 2019, several models have been developed to study and understand its transmission dynamics. Although the spread of COVID-19 is being slowed down by vaccination and other interventions, there is still a need to have a clear understanding of the evolution of the pandemic across countries, states and communities. To this end, there is a need to have a clearer picture of the initial spread of the disease in different regions. In this project, we used a simple SEIR model and a Bayesian inference framework to estimate the basic reproduction number of COVID-19 across Africa. Our estimates vary between 1.98 (Sudan) and 9.66 (Mauritius), with a median of 3.67 (90% CrI: 3.31-4.12). The estimates provided in this paper will help to inform COVID-19 modeling in the respective countries/regions.

Effect of hydro-geochemical processes and saltwater intrusion on groundwater quality and irrigational suitability assessed by geo-statistical techniques in coastal region of eastern Andhra Pradesh, India

Sustainable management of groundwater needs comprehensive study on water quality in present scenario. Hence, an understanding on the hydro geochemistry, saltwater intrusion, spatiotemporal-seasonal variations and irrigational suitability of groundwater becomes a must, especially in coastal regions. Our study area is one such place where all the parameters play a major role against sustainable management. The study pointed out that majority of the samples is brackish with two notable geochemical facies for pre monsoon and post monsoon. Factor and cluster analyses revealed that EC, TDS, Na⁺, Cl^-, Mg²⁺ and Ca²⁺ are the major contributors. Gibb's diagram supported the dominance of rock weathering and evaporation in controlling the groundwater chemistry. Sea water intrusion was mapped using HFE diagrams and the Irrigational suitability is studied using USSL, SAR, %Na, etc. The data and results from this study might provide crucial information to water management authorities in dealing groundwater scarcity and pollution problems.

COVID-19 and beyond: Use of digital technology for pandemic response in Africa

The use of technology has been ubiquitous in efforts to combat the ongoing COVID-19 pandemic. In this perspective, we review technologies and new approaches developed at the start of the pandemic; efforts earmarked by a flexible approach to problem solving, local tech entrepreneurship, and swift adoption of technology. We performed a systematic review of the use of technology during the initial wave of the COVID-19 pandemic in most African countries. We identified relevant articles by searching for mentions of technology, COVID-19, and specific country names. Articles were included if they specifically mentioned the use of technology or novel innovations in the response to the COVID-19 pandemic in an African country. The article search was conducted in August and included articles published between January and August 2020. We retrieved articles from journals, trusted news, government, and organization websites on Google, Google Scholar and PubMed. A total of 80 articles were retained and categorized under Disease Prevention (19 articles), Disease Surveillance xxx Antipoaching Tech Tracks COVID-19 Flare-Ups in South Africa - Scientific American. (2020, May 12), and Clinical Supplies and Management xxx Ethiopia's digital health response to COVID-19 - JSI. (2020, May 14). African nations used technology and innovative techniques to manage patients, monitor cases and disseminate information to counter the spread of COVID-19. The nature and outcomes of these efforts sometimes differed in Africa compared to other regions of the world due to its unique challenges and opportunities.

A review of GIS methodologies to analyze the dynamics of COVID-19 in the second half of 2020

COVID-19 has infected over 163 million people and has resulted in over 3.9 million deaths. Regarding the tools and strategies to research the ongoing pandemic, spatial analysis has been increasingly utilized to study the impacts of COVID-19. This article provides a review of 221 scientific articles that used spatial science to study the pandemic published from June 2020 to December 2020. The main objectives are: to identify the tools and techniques used by the authors; to review the subjects addressed and their disciplines; and to classify the studies based on their applications. This contribution will facilitate comparisons with the body of work published during the first half of 2020, revealing the evolution of the COVID-19 phenomenon through the lens of spatial analysis. Our results show that there was an increase in the use of both spatial statistical tools (e.g., geographically weighted regression, Bayesian models, spatial regression) applied to socioeconomic variables and analysis at finer spatial and temporal scales. We found an increase in remote sensing approaches, which are now widely applied in studies around the world. Lockdowns and associated changes in human mobility have been extensively examined using spatiotemporal techniques. Another dominant topic studied has been the relationship between pollution and COVID-19 dynamics, which enhance the impact of human activities on the pandemic's evolution. This represents a shift from the first half of 2020, when the research focused on climatic and weather factors. Overall, we have seen a vast increase in spatial tools and techniques to study COVID-19 transmission and the associated risk factors.

Knowledge and preventive practices towards COVID-19 among pregnant women seeking antenatal services in Northern Ghana

BACKGROUND

COVID-19 is a novel respiratory disease associated with severe morbidity and high mortality in the elderly population and people with comorbidities. Studies have suggested that pregnant women are more susceptible to COVID-19 compared to non-pregnant women. However, it's unclear whether pregnant women in Ghana are knowledgeable about COVID-19 and practice preventive measures against it. This study sought to assess the knowledge and preventive practices towards COVID-19 among pregnant women seeking antenatal services in Northern Ghana.

METHODS

A cross-sectional study was conducted using a structured questionnaire in the Nabdam district in Ghana. A total of 527 pregnant women were randomly sampled from health facilities offering antenatal care services in the district. Multivariable logistic regression analysis was used to assess the association between the predictors and outcome variables.

RESULTS

The prevalence of adequate knowledge and good COVID-19 preventive practices were 85.6%, (95% CI: 82.57, 88.59) and 46.6%, (95% CI: 42.41, 50.95) respectively. Having at least a primary education, residing in an urban area, and receiving COVID-19 education at a health facility were positively associated with adequate knowledge on COVID-19. Factors positively associated with good COVID-19 preventive practices were older age, having at least a primary education, pregnant women with a chronic disease, and living in an urban area. Multiparity was negatively associated with good COVID-19 preventive practices.

CONCLUSION

Although majority of women had adequate knowledge of COVID-19, less than half of them were engaged in good COVID-19 preventive practices. Education of pregnant women on COVID-19 preventive practices should be intensified at health facilities while improving upon the water, sanitation and hygiene need particularly in rural communities.

Adherence to COVID-19 preventive measures and associated factors among pregnant women in Ghana

OBJECTIVE

To assess adherence to COVID-19 preventive measures and its associated factors among pregnant women in Ghana.

METHODS

This was a cross-sectional study conducted in the Nabdam district, Ghana. Data were collected from 527 pregnant women randomly selected from antenatal care clinics from 16 healthcare facilities. Descriptive statistics were used to assess the prevalence of adherence to COVID-19 preventive measures. Multivariable logistic regression was used to estimate the factors associated with COVID-19 preventive measures, whilst adjusting for potential confounders.

RESULTS

The prevalence of wearing a face mask 18.0% (95% CI: 14.73%, 21.32%); of handwashing/hand sanitising 31.7% (95% CI: 27.70%, 35.67%), and of social distancing, 22.0% (95% CI: 18.46%, 25.56%). Multivariable logistic regression analysis revealed that knowledge of COVID-19 symptoms [Adjusted odds ratios (aOR): 2.86, 95% CI: 1.03, 7.89] and knowledge of COVID-19 transmission via contaminated surfaces/objects (aOR: 4.60, 95% CI: 1.23, 17.18) were associated with wearing a face mask. Pregnant women who knew that avoiding the touching of eyes, nose and mouth can prevent COVID-19 (aOR: 2.71, 95% CI: 1.01, 7.28), and knowledge of the virus being transmitted via contaminated objects/surfaces (aOR: 4.08, 95% CI: 1.42, 11.76), were associated with handwashing/hand sanitising. Knowledge of COVID-19 transmission via contaminated surfaces/objects (aOR: 15.27, 95% CI: 1.87, 124.43) was also associated with social distancing.

CONCLUSION

The findings of our study suggest that knowledge of COVID-19 symptoms, transmission and preventive measures may play an important role in the practice of preventive measures against COVID-19 among pregnant women.

A nationwide survey of the potential acceptance and determinants of COVID-19 vaccines in Ghana

PURPOSE

Safe and effective vaccine together with better treatment remains one of the strategic exist of the coronavirus disease 2019 (COVID-19) pandemic. As in many other countries worldwide, the government of Ghana has expressed its commitment to procuring globally approved and accepted vaccines. This preliminary study aims to analyses these factors that could impact the choice of the vaccine in Ghana.

MATERIALS AND METHODS

This cross-sectional survey was conducted among 1,000 Ghanaian respondents from the 14th October to the 12th of December 2020. A structured questionnaire after a series of literature review and was transcribed unto google forms. Dataset was extracted using Excel ver. 2016 (Microsoft Corp., Redmond, WA, USA) and imported into IBM SPSS ver. 22.0 (IBM Corp., Armonk, NY, USA) for analysis. Descriptive statistics, cross-tabulations and logistic regression analyses were conducted at p-value <0.05.

RESULTS

The results showed that 541 (54.1%) of the respondents would opt for the vaccines, 907 (90.7%) trusted the healthcare system and 388 (38.8%) had a high-risk perception of acquiring COVID-19. Our inferential analysis found that being married (adjusted odds ratio [aOR], 2.29; 95% confidence interval [CI], 1.10-4.78), salary worker (private: aOR, 2.24; 95% CI, 0.85-5.89; government: aOR, 42.58; 95% CI, 14.09-128.61), and high-risk perception (aOR, 5.35; 95% CI, 3.24-8.82) had a higher OR of accepting COVID-19 vaccine.

CONCLUSION

As the government of Ghana prepares to secure COVID-19 vaccines, it is important to understand the state of mind and determinants of COVID-19 vaccine acceptance in the general population. Policymakers and stakeholders should focus on evidence-based community messaging to improve uptake and break the transmission dynamics.

Geographic information system-based analysis of COVID-19 cases in India during pre-lockdown, lockdown, and unlock phases

OBJECTIVE

The World Health Organization formally announced the global COVID-19 pandemic on March 11, 2020 due to widespread infections. In this study, COVID-19 cases in India were critically analyzed during the pre-lockdown (PLD), lockdown (LD), and unlock (UL) phases.

METHOD

Analyses were conducted using geospatial technology at district, state, and country levels, and comparisons were also made with other countries throughout the world that had the highest infection rates. India had the third highest infection rate in the world after the USA and Brazil during UL2.0-UL3.0 phases, the second highest after the USA during UL4.0-UL5.0 phases, and the highest among South Asian Association for Regional Cooperation (SAARC) countries in PLD-UL5.0 period.

RESULTS

The trend in the number of COVID-19 cases was associated with the population density where higher numbers tended to be record in the eastern, southern, and west-central parts of India. The death rate in India throughout the pandemic period under study was lower than the global average. Kerala reported the maximum number of infections during PLD whereas Maharashtra had the highest numbers during all LD and UL phases. Eighty percent of the cases in India were concentrated mainly in highly populous districts.

CONCLUSION

The top 25 districts accounted for 70.99%, 69.38%, 54.87%, 44.23%, 40.48%, and 38.96% of the infections from the start of UL1.0 until the end of UL phases, respectively, and the top 26-50 districts accounted for 6.38%, 6.76%, 11.23%, 12.98%, 13.40%, and 13.61% of cases in these phase, thereby indicating that COVID-19 cases spread during the UL period. By October 31, 2020, Delhi had the highest number of infections, followed by Bengaluru Urban, Pune, Mumbai, Thane, and Chennai. No decline in the infection rate occurred, even in UL5.0, thereby indicating a highly alarming situation in India.

Spatiotemporal Analysis of COVID-19 Spread with Emerging Hotspot Analysis and Space–Time Cube Models in East Java, Indonesia

In this research, we analyzed COVID-19 distribution patterns based on hotspots and space–time cubes (STC) in East Java, Indonesia. The data were collected based on the East Java COVID-19 Radar report results from a four-month period, namely March, April, May, and June 2020. Hour, day, and date information were used as the basis of the analysis. We used two spatial analysis models: the emerging hotspot analysis and STC. Both techniques allow us to identify the hotspot cluster temporally. Three-dimensional visualizations can be used to determine the direction of spread of COVID-19 hotspots. The results showed that the spread of COVID-19 throughout East Java was centered in Surabaya, then mostly spread towards suburban areas and other cities. An emerging hotspot analysis was carried out to identify the patterns of COVID-19 hotspots in each bin. Both cities featured oscillating patterns and sporadic hotspots that accumulated over four months. This pattern indicates that newly infected patients always follow the recovery of previous COVID-19 patients and that the increase in the number of positive patients is higher when compared to patients who recover. The monthly hotspot analysis results yielded detailed COVID-19 spatiotemporal information and facilitated more in-depth analysis of events and policies in each location/time bin. The COVID-19 hotspot pattern in East Java, visually speaking, has an amoeba-like pattern. Many positive cases tend to be close to the city, in places with high road density, near trade and business facilities, financial storage, transportation, entertainment, and food venues. Determining the spatial and temporal resolution for the STC model is crucial because it affects the level of detail for the information of endemic disease distribution and is important for the emerging hotspot analysis results. We believe that similar research is still rare in Indonesia, although it has been done elsewhere, in different contexts and focuses.

Spatial temporal distribution of COVID-19 risk during the early phase of the pandemic in Malawi

BACKGROUND

COVID-19 has been one of the greatest challenges the world has faced since the second world war. This study aimed at investigating the distribution of COVID-19 in both space and time in Malawi.

METHODS

The study used publicly available data of COVID-19 cases for the period from 2 April 2020 to 28 October 2020. Semiparametric spatial temporal models were fitted to the number of monthly confirmed cases as an outcome data, with time and district as independent variables, where district was the spatial unit, while accounting for sociodemographic factors.

RESULTS

The study found significant effects of location and time, with the two interacting. The spatial distribution of COVID-19 risk showed major cities being at greater risk than rural areas. Over time, the COVID-19 risk was increasing then decreasing in most districts with the rural districts being consistently at lower risk. High proportion of elderly people was positively associated with COVID-19 risk (β = 1.272, 95% CI [0.171, 2.370]) than low proportion of elderly people. There was negative association between poverty incidence and COVID-19 risk (β = -0.100, 95% CI [-0.136, -0.065]).

CONCLUSION

Future or present strategies to limit the spread of COVID-19 should target major cities and the focus should be on time periods that had shown high risk. Furthermore, the focus should be on elderly and rich people.

Modeling Transmission Dynamics and Risk Assessment for COVID-19 in Namibia Using Geospatial Technologies

The SARS-CoV-2 infections continue to increase in Namibia and globally. Assessing and mapping the COVID-19 risk zones and modeling the response of COVID-19 using different scenarios are very vital to help decision-makers to estimate the immediate number of resources needed and plan for future interventions of COVID-19 in the area of interest. This study is aimed to identify and map COVID-19 risk zones and to model future COVID-19 response of Namibia using geospatial technologies. Population density, current COVID-19 infections, and spatial interaction index were used as proxy data to identify the different COVID-19 risk zones of Namibia. COVID-19 Hospital Impact Model for Epidemics (CHIME) V1.1.5 tool was used to model future COVID-19 responses with mobility restrictions. Weights were assigned for each thematic layer and thematic layer classes using the Analytical Hierarchy Process (AHP) tool. Suitably ArcGIS overlay analysis was conducted to produce risk zones. Current COVID-19 infection and spatial mobility index were found to be the dominant and sensitive factors for risk zoning in Namibia. Six different COVID-19 risk zones were identified in the study area, namely highest, higher, high, low, lower, and lowest. Modeling result revealed that mobility reduction by 30% within the country had a notable effect on controlling COVID-19 spread: a flattening of the peak number of cases and delay to the peak number. The research output could help policy-makers to estimate the immediate number of resources needed and plan for future interventions of COVID-19 in Namibia, especially to assess the potential positive effects of mobility restriction.

Dashboard COMPRIME_COMPRI_MOv: Multiscalar Spatio-Temporal Monitoring of the COVID-19 Pandemic in Portugal

Due to its novelty, the recent pandemic of the coronavirus disease (COVID-19), which is associated with the spread of the new severe acute respiratory syndrome coronavirus (SARS-CoV-2), triggered the public’s interest in accessing information, demonstrating the importance of obtaining and analyzing credible and updated information from an epidemiological surveillance context. For this purpose, health authorities, international organizations, and university institutions have published online various graphic and cartographic representations of the evolution of the pandemic with daily updates that allow the almost real-time monitoring of the evolutionary behavior of the spread, lethality, and territorial distribution of the disease. The purpose of this article is to describe the technical solution and the main results associated with the publication of the COMPRIME_COMPRI_MOv dashboard for the dissemination of information and multi-scale knowledge of COVID-19. Under two rapidly implementing research projects for innovative solutions to respond to the COVID-19 pandemic, promoted in Portugal by the FCT (Foundation for Science and Technology), a website was created. That website brings together a diverse set of variables and indicators in a dynamic and interactive way that reflects the evolutionary behavior of the pandemic from a multi-scale perspective, in Portugal, constituting itself as a system for monitoring the evolution of the pandemic. In the current situation, this type of exploratory solutions proves to be crucial to guarantee everyone’s access to information while simultaneously emerging as an epidemiological surveillance tool that is capable of assisting decision-making by public authorities with competence in defining control policies and fight the spread of the new coronavirus.

A critical review of emerging technologies for tackling COVID-19 pandemic

COVID-19 pandemic affects people in various ways and continues to spread globally. Researches are ongoing to develop vaccines and traditional methods of Medicine and Biology have been applied in diagnosis and treatment. Though there are success stories of recovered cases as of November 10, 2020, there are no approved treatments and vaccines for COVID-19. As the pandemic continues to spread, current measures rely on prevention, surveillance, and containment. In light of this, emerging technologies for tackling COVID-19 become inevitable. Emerging technologies including geospatial technology, artificial intelligence (AI), big data, telemedicine, blockchain, 5G technology, smart applications, Internet of Medical Things (IoMT), robotics, and additive manufacturing are substantially important for COVID-19 detecting, monitoring, diagnosing, screening, surveillance, mapping, tracking, and creating awareness. Therefore, this study aimed at providing a comprehensive review of these technologies for tackling COVID-19 with emphasis on the features, challenges, and country of domiciliation. Our results show that performance of the emerging technologies is not yet stable due to nonavailability of enough COVID-19 dataset, inconsistency in some of the dataset available, nonaggregation of the dataset due to contrasting data format, missing data, and noise. Moreover, the security and privacy of people's health information is not totally guaranteed. Thus, further research is required to strengthen the current technologies and there is a strong need for the emergence of a robust computationally intelligent model for early differential diagnosis of COVID-19.

Integrating emerging technologies into COVID-19 contact tracing: Opportunities, challenges and pitfalls

BACKGROUND AND AIMS

With no approved vaccines for treating COVID-19 as of August 2020, many health systems and governments rely on contact tracing as one of the prevention and containment methods. However, there have been instances when the infected person forgets his/her contact-persons and does not have their contact details. Therefore, this study aimed at analyzing possible opportunities and challenges of integrating emerging technologies into COVID-19 contact tracing.

METHODS

The study applied literature search from Google Scholar, Science Direct, PubMed, Web of Science, IEEE and WHO COVID-19 reports and guidelines analyzed.

RESULTS

While the integration of technology-based contact tracing applications to combat COVID-19 and break transmission chains promise to yield better results, these technologies face challenges such as technical limitations, dealing with asymptomatic individuals, lack of supporting ICT infrastructure and electronic health policy, socio-economic inequalities, deactivation of mobile devices' WIFI, GPS services, interoperability and standardization issues, security risks, privacy issues, political and structural responses, ethical and legal risks, consent and voluntariness, abuse of contact tracing apps, and discrimination.

CONCLUSION

Integrating emerging technologies into COVID-19 contact tracing is seen as a viable option that policymakers, health practitioners and IT technocrats need to seriously consider in mitigating the spread of coronavirus. Further research is also required on how best to improve efficiency and effectiveness in the utilisation of emerging technologies in contact tracing while observing the security and privacy of people in fighting the COVID-19 pandemic.