New technologies in predicting, preventing and controlling emerging infectious diseases

Spatiotemporal distribution of climate-sensitive disease incidences in ethiopia: a longitudinal retrospective analysis of Malaria, Meningitis, Cholera, Dysentery, Leishmaniasis and Dengue fever between 2010 and 2022/2023

BACKGROUND

Understanding the temporal and geographic distribution of disease incidences is crucial for effective public health planning and intervention strategies. This study presents a comprehensive analysis of the spatiotemporal distribution of disease incidences in Ethiopia, focusing on six major diseases: Malaria, Meningitis, Cholera and Dysentery, over the period from 2010 to 2022, whereas Dengue Fever and Leishmaniasis from 2018 to 2023.

METHODS

Using data from Ethiopian public health institute: public health emergency management (PHEM), and Ministry of Health, we examined the occurrence and spread of each disease across different regions of Ethiopia. Spatial mapping and time series analysis were employed to identify hotspots, trends, and seasonal variations in disease incidence.

RESULTS

The findings reveal distinct patterns for each disease, with varying cases and temporal dynamics. Monthly wise, Malaria exhibits a cyclical pattern with a peak during the rainy and humid season, while Dysentery, Meningitis and Cholera displays intermittent incidences. Dysentery cases show a consistent presence throughout the years, while Meningitis remains relatively low in frequency but poses a potential threat due to its severity. Dengue fever predominantly occurs in the eastern parts of Ethiopia. A significant surge in reported incident cases occurred during the years 2010 to 2013, primarily concentrated in the Amhara, Sidama, Oromia, Dire Dawa, and Benishangul-Gumuz regions.

CONCLUSIONS

This study helps to a better understanding of disease epidemiology in Ethiopia and can serve as a foundation for evidence-based decision-making in disease prevention and control. By recognizing the patterns and seasonal changes associated with each disease, health authorities can implement proactive measures to mitigate the impact of outbreaks and safeguard public health in the region.

Emerging infectious disease surveillance using a hierarchical diagnosis model and the Knox algorithm

Emerging infectious diseases are a critical public health challenge in the twenty-first century. The recent proliferation of such diseases has raised major social and economic concerns. Therefore, early detection of emerging infectious diseases is essential. Subjects from five medical institutions in Beijing, China, which met the spatial-specific requirements, were analyzed. A quality control process was used to select 37,422 medical records of infectious diseases and 56,133 cases of non-infectious diseases. An emerging infectious disease detection model (EIDDM), a two-layer model that divides the problem into two sub-problems, i.e., whether a case is an infectious disease, and if so, whether it is a known infectious disease, was proposed. The first layer model adopts the binary classification model TextCNN-Attention. The second layer is a multi-classification model of LightGBM based on the one-vs-rest strategy. Based on the experimental results, a threshold of 0.5 is selected. The model results were compared with those of other models such as XGBoost and Random Forest using the following evaluation indicators: accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. The prediction performance of the first-layer TextCNN is better than that of other comparison models. Its average specificity for non-infectious diseases is 97.57%, with an average negative predictive value of 82.63%, indicating a low risk of misdiagnosing non-infectious diseases as infectious (i.e., a low false positive rate). Its average positive predictive value for eight selected infectious diseases is 95.07%, demonstrating the model's ability to avoid misdiagnoses. The overall average accuracy of the model is 86.11%. The average prediction accuracy of the second-layer LightGBM model for emerging infectious diseases reaches 90.44%. Furthermore, the response time of a single online reasoning using the LightGBM model is approximately 27 ms, which makes it suitable for analyzing clinical records in real time. Using the Knox method, we found that all the infectious diseases were within 2000 m in our case, and a clustering feature of spatiotemporal interactions (P < 0.05) was observed as well. Performance testing and model comparison results indicated that the EIDDM is fast and accurate and can be used to monitor the onset/outbreak of emerging infectious diseases in real-world hospitals.

A global examination of ecological niche modeling to predict emerging infectious diseases: a systematic review

Introduction

As emerging infectious diseases (EIDs) increase, examining the underlying social and environmental conditions that drive EIDs is urgently needed. Ecological niche modeling (ENM) is increasingly employed to predict disease emergence based on the spatial distribution of biotic conditions and interactions, abiotic conditions, and the mobility or dispersal of vector-host species, as well as social factors that modify the host species' spatial distribution. Still, ENM applied to EIDs is relatively new with varying algorithms and data types. We conducted a systematic review (PROSPERO: CRD42021251968) with the research question: What is the state of the science and practice of estimating ecological niches via ENM to predict the emergence and spread of vector-borne and/or zoonotic diseases?

Methods

We searched five research databases and eight widely recognized One Health journals between 1995 and 2020. We screened 383 articles at the abstract level (included if study involved vector-borne or zoonotic disease and applied ENM) and 237 articles at the full-text level (included if study described ENM features and modeling processes). Our objectives were to: (1) describe the growth and distribution of studies across the types of infectious diseases, scientific fields, and geographic regions; (2) evaluate the likely effectiveness of the studies to represent ecological niches based on the biotic, abiotic, and mobility framework; (3) explain some potential pitfalls of ENM algorithms and techniques; and (4) provide specific recommendation for future studies on the analysis of ecological niches to predict EIDs.

Results

We show that 99% of studies included mobility factors, 90% modeled abiotic factors with more than half in tropical climate zones, 54% modeled biotic conditions and interactions. Of the 121 studies, 7% include only biotic and mobility factors, 45% include only abiotic and mobility factors, and 45% fully integrated the biotic, abiotic, and mobility data. Only 13% of studies included modifying social factors such as land use. A majority of studies (77%) used well-recognized ENM algorithms (MaxEnt and GARP) and model selection procedures. Most studies (90%) reported model validation procedures, but only 7% reported uncertainty analysis.

Discussion

Our findings bolster ENM to predict EIDs that can help inform the prevention of outbreaks and future epidemics.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier (CRD42021251968).

Relevance of national, regional and global virome projects on pandemics prediction, prevention, and control: a social network analysis of GVP-citing articles

BACKGROUND

The Global Virome Project (GVP) was proposed in 2018 as an evolution of the USAID PREDICT project and was presented as a "collaborative scientific initiative to discover zoonotic viral threats and stop future pandemics". The immediate response was mixed, with public health and scientific communities representatives showing skepticism, if not direct opposition.

OBJECTIVES

The economic, social, and health consequences of the coronavirus disease 2019 (COVID-19) pandemic demonstrated how unprepared the world was in the face of new pandemics. This paper analyses the impact of the GVP on the scientific and public health communities.

METHODS

Published scientific articles that cited the two 2018 seminal publications proposing the project were analysed using social network analysis methods.

FINDINGS

Encompassing the periods before and after the onset of the Covid-19 pandemic, the results indicate that (i) the concepts of the GVP have received more support than opposition in the scientific literature; (ii) its foundations should be updated to address the specific criticisms.

MAIN CONCLUSIONS

Shifting focus to national virome projects can provide tangible, regional benefits that can positively contribute towards a consensus on achieving a high level of preparedness for the ever-present possibility of the following global viral pandemic.

Epidemiological evidence of acute transmission of Zika virus infection in dengue suspected patients in Sri-Lanka

BACKGROUND

Zika Virus (ZIKV) is a re-emerging, arthropod-borne flavivirus transmitted by Aedes mosquitoes (Ae. aegypti and Ae. albopictus). The coexistence of dengue virus (DENV) and ZIKV concurrently has been associated with a wide array of neurological complications, which may influence the clinical outcomes of infections. Sri Lanka witnessed a severe dengue epidemic in 2017, characterized by extraordinary and severe disease manifestations with considerable morbidity. Therefore, this study assessed the potential occurrence of ZIKV infection during DENV outbreak in Sri Lanka from 2017 to 2019, which could bear substantial implications for public health.

METHODS

Five hundred ninety-five serum samples were procured from individuals suspected of dengue and admitted to Kandy National Hospital between 2017 and 2018 and the Negombo District General Hospital between 2018 and 2019. These samples underwent quantitative real-time RT-PCR (qRT-PCR) to identify the presence of the ZIKV gene, while enzyme-linked immunosorbent assay was employed to detect ZIKV-specific IgM and IgG antibodies. Focus reduction neutralization tests were subsequently conducted to confirm ZIKV infection.

RESULTS

Among the 595 serum samples, 6 (1.0%) tested positive for ZIKV using qRT-PCR. Anti-ZIKV IgM and IgG were identified in 18.0% and 38.6% patients. Sixty-six (11.0%) samples demonstrated the presence of anti-ZIKV IgM and IgG. Within ZIKV IgM-positive samples, 2.2% exhibited neutralizing antibodies against ZIKV. Through the implementation of qRT-PCR, ZIKV IgM detection, and neutralization testing, 2% and 3.7% cases of ZIKV infections were confirmed in the Kandy and Negombo regions, respectively.

CONCLUSION

This study is the inaugural endeavor to substantiate the existence of ZIKV infection in Sri Lanka utilizing molecular and serological analysis. The findings of this investigation imply that ZIKV was circulating throughout the 2017-2019 DENV outbreak. These results underscore the necessity for improved preparedness for future outbreaks, fortifying governmental policies on public health, and establishing effective early warning systems regarding the emergence of these viruses.

Responsible design and implementation of technologies for the prevention of infectious diseases: towards a values-based assessment framework for the Dutch government

OBJECTIVES

The Dutch government implemented the apps 'CoronaMelder' and 'CoronaCheck' to prevent the transmission of SARS-CoV-2. They faced many questions on how to responsibly implement such technologies. Here, we aim to develop an assessment framework to support the Dutch national government with the responsible design and implementation of technologies for the prevention of future infectious diseases.

STUDY DESIGN

Three-stage web-based Delphi process.

METHODS

The assessment framework was developed through two research phases. During the Initial Design phase, a conceptual version of the assessment framework was developed through a scoping review and semistructured interviews with a scientific board. The Consensus phase involved a three-stage web-based Delphi process with an expert community.

RESULTS

The final assessment framework consists of five development phases, 10 values, and a total of 152 questions.

CONCLUSIONS

Technology assessment frameworks help policymakers to make informed decisions and contribute to the responsible implementation of technologies in society. The framework is now available for the Dutch government and other stakeholders to use in future pandemics. We discuss the possibilities of using the framework transnationally.

The applicability of human mobility scaling laws on animals-A Herring Gull case study

With the development of sensors, recording and availability of high-resolution movement data from animals and humans, two disciplines have rapidly developed: human mobility and movement ecology. Addressing methodological gaps between these two mobility fields could improve the understanding of movement processes and has been defined as the Integrated Science of Movement. We apply well-known human mobility metrics and data processing methods to Global Positioning System (GPS) tracking data of European Herring Gulls (Larus argentatus) to test the usefulness of these methods for explaining animal mobility behavior. We use stop detection, spatial aggregation, and for the first time on animal movement data, two approaches to temporal aggregation (Next Time-Bin and Next Place). We also calculate from this data a set of movement statistics (visitation frequency, distinct locations over time, and radius of gyration). Furthermore, we analyze and compare the gull and human data from the perspective of scaling laws commonly used for human mobility. The results confirm those of previous studies and indicate differences in movement parameters between the breeding season and other parts of the year. This paper also shows that methods used in human mobility analysis have the potential to improve our understanding of animal behavior.

A novel authentication protocol to ensure confidentiality among the Internet of Medical Things in covid-19 and future pandemic scenario

Diagnosing the patients remotely, controlling the medical equipment, and monitoring the quarantined patients are some of the necessary and frequent activities in COVID-19. Internet of Medical Things (IoMT) makes this works easy and feasible. Sharing information from patients and sensors associated with the patients to doctors is always an integral part of IoMT. Unauthorized access to such information may invite adversaries to disturb patients financially and mentally; furthermore, leaks in its confidentiality will lead to dangerous health concerns for patients. While ensuring authentication and confidentiality, We must focus on the constraints of IoMT, such as low energy consumption, deficient memory, and the dynamic nature of devices. Numerous protocols have been proposed for authentication in healthcare systems such as IoMT and telemedicine. However, many of these protocols were neither computationally efficient nor provided confidentiality, anonymity, and resistance against several attacks. In the proposed protocol, we have considered the most common scenario of IoMT and tried to overcome the limitations of existing works. Describing the system module and security analysis proves it is a panacea for COVID-19 and future pandemics.

Travel-Related Diagnoses Among U.S. Nonmigrant Travelers or Migrants Presenting to U.S. GeoSentinel Sites - GeoSentinel Network, 2012-2021

Problem/Condition

During 2012-2021, the volume of international travel reached record highs and lows. This period also was marked by the emergence or large outbreaks of multiple infectious diseases (e.g., Zika virus, yellow fever, and COVID-19). Over time, the growing ease and increased frequency of travel has resulted in the unprecedented global spread of infectious diseases. Detecting infectious diseases and other diagnoses among travelers can serve as sentinel surveillance for new or emerging pathogens and provide information to improve case identification, clinical management, and public health prevention and response.

Reporting Period

2012-2021.

Description of System

Established in 1995, the GeoSentinel Network (GeoSentinel), a collaboration between CDC and the International Society of Travel Medicine, is a global, clinical-care-based surveillance and research network of travel and tropical medicine sites that monitors infectious diseases and other adverse health events that affect international travelers. GeoSentinel comprises 71 sites in 29 countries where clinicians diagnose illnesses and collect demographic, clinical, and travel-related information about diseases and illnesses acquired during travel using a standardized report form. Data are collected electronically via a secure CDC database, and daily reports are generated for assistance in detecting sentinel events (i.e., unusual patterns or clusters of disease). GeoSentinel sites collaborate to report disease or population-specific findings through retrospective database analyses and the collection of supplemental data to fill specific knowledge gaps. GeoSentinel also serves as a communications network by using internal notifications, ProMed alerts, and peer-reviewed publications to alert clinicians and public health professionals about global outbreaks and events that might affect travelers. This report summarizes data from 20 U.S. GeoSentinel sites and reports on the detection of three worldwide events that demonstrate GeoSentinel's notification capability.

Results

During 2012-2021, data were collected by all GeoSentinel sites on approximately 200,000 patients who had approximately 244,000 confirmed or probable travel-related diagnoses. Twenty GeoSentinel sites from the United States contributed records during the 10-year surveillance period, submitting data on 18,336 patients, of which 17,389 lived in the United States and were evaluated by a clinician at a U.S. site after travel. Of those patients, 7,530 (43.3%) were recent migrants to the United States, and 9,859 (56.7%) were returning nonmigrant travelers.Among the recent migrants to the United States, the median age was 28.5 years (range = <19 years to 93 years); 47.3% were female, and 6.0% were U.S. citizens. A majority (89.8%) were seen as outpatients, and among 4,672 migrants with information available, 4,148 (88.8%) did not receive pretravel health information. Of 13,986 diagnoses among migrants, the most frequent were vitamin D deficiency (20.2%), Blastocystis (10.9%), and latent tuberculosis (10.3%). Malaria was diagnosed in 54 (<1%) migrants. Of the 26 migrants diagnosed with malaria for whom pretravel information was known, 88.5% did not receive pretravel health information. Before November 16, 2018, patients' reasons for travel, exposure country, and exposure region were not linked to an individual diagnosis. Thus, results of these data from January 1, 2012, to November 15, 2018 (early period), and from November 16, 2018, to December 31, 2021 (later period), are reported separately. During the early and later periods, the most frequent regions of exposure were Sub-Saharan Africa (22.7% and 26.2%, respectively), the Caribbean (21.3% and 8.4%, respectively), Central America (13.4% and 27.6%, respectively), and South East Asia (13.1% and 16.9%, respectively). Migrants with diagnosed malaria were most frequently exposed in Sub-Saharan Africa (89.3% and 100%, respectively).Among nonmigrant travelers returning to the United States, the median age was 37 years (range = <19 years to 96 years); 55.7% were female, 75.3% were born in the United States, and 89.4% were U.S. citizens. A majority (90.6%) were seen as outpatients, and of 8,967 nonmigrant travelers with available information, 5,878 (65.6%) did not receive pretravel health information. Of 11,987 diagnoses, the most frequent were related to the gastrointestinal system (5,173; 43.2%). The most frequent diagnoses among nonmigrant travelers were acute diarrhea (16.9%), viral syndrome (4.9%), and irritable bowel syndrome (4.1%).Malaria was diagnosed in 421 (3.5%) nonmigrant travelers. During the early (January 1, 2012, to November 15, 2018) and later (November 16, 2018, to December 31, 2021) periods, the most frequent reasons for travel among nonmigrant travelers were tourism (44.8% and 53.6%, respectively), travelers visiting friends and relatives (VFRs) (22.0% and 21.4%, respectively), business (13.4% and 12.3%, respectively), and missionary or humanitarian aid (13.1% and 6.2%, respectively). The most frequent regions of exposure for any diagnosis among nonmigrant travelers during the early and later period were Central America (19.2% and 17.3%, respectively), Sub-Saharan Africa (17.7% and 25.5%, respectively), the Caribbean (13.0% and 10.9%, respectively), and South East Asia (10.4% and 11.2%, respectively).Nonmigrant travelers who had malaria diagnosed were most frequently exposed in Sub-Saharan Africa (88.6% and 95.9% during the early and later period, respectively) and VFRs (70.3% and 57.9%, respectively). Among VFRs with malaria, a majority did not receive pretravel health information (70.2% and 83.3%, respectively) or take malaria chemoprophylaxis (88.3% and 100%, respectively).

Interpretation

Among ill U.S. travelers evaluated at U.S. GeoSentinel sites after travel, the majority were nonmigrant travelers who most frequently received a gastrointestinal disease diagnosis, implying that persons from the United States traveling internationally might be exposed to contaminated food and water. Migrants most frequently received diagnoses of conditions such as vitamin D deficiency and latent tuberculosis, which might result from adverse circumstances before and during migration (e.g., malnutrition and food insecurity, limited access to adequate sanitation and hygiene, and crowded housing,). Malaria was diagnosed in both migrants and nonmigrant travelers, and only a limited number reported taking malaria chemoprophylaxis, which might be attributed to both barriers to acquiring pretravel health care (especially for VFRs) and lack of prevention practices (e.g., insect repellant use) during travel. The number of ill travelers evaluated by U.S. GeoSentinel sites after travel decreased in 2020 and 2021 compared with previous years because of the COVID-19 pandemic and associated travel restrictions. GeoSentinel detected limited cases of COVID-19 and did not detect any sentinel cases early in the pandemic because of the lack of global diagnostic testing capacity.

Public Health Action

The findings in this report describe the scope of health-related conditions that migrants and returning nonmigrant travelers to the United States acquired, illustrating risk for acquiring illnesses during travel. In addition, certain travelers do not seek pretravel health care, even when traveling to areas in which high-risk, preventable diseases are endemic. Health care professionals can aid international travelers by providing evaluations and destination-specific advice.Health care professionals should both foster trust and enhance pretravel prevention messaging for VFRs, a group known to have a higher incidence of serious diseases after travel (e.g., malaria and enteric fever). Health care professionals should continue to advocate for medical care in underserved populations (e.g., VFRs and migrants) to prevent disease progression, reactivation, and potential spread to and within vulnerable populations. Because both travel and infectious diseases evolve, public health professionals should explore ways to enhance the detection of emerging diseases that might not be captured by current surveillance systems that are not site based.

Fuzzy-logic-based IoMT framework for COVID19 patient monitoring

Smart healthcare is an integral part of a smart city, which provides real time and intelligent remote monitoring and tracking services to patients and elderly persons. In the era of an extraordinary public health crisis due to the spread of the novel coronavirus (2019-nCoV), which caused the deaths of millions and affected a multitude of people worldwide in different ways, the role of smart healthcare has become indispensable. Any modern method that allows for speedy and efficient monitoring of COVID19-affected patients could be highly beneficial to medical staff. Several smart-healthcare systems based on the Internet of Medical Things (IoMT) have attracted worldwide interest in their growing technical assistance in health services, notably in predicting, identifying and preventing, and their remote surveillance of most infectious diseases. In this paper, a real time health monitoring system for COVID19 patients based on edge computing and fuzzy logic technique is proposed. The proposed model makes use of the IoMT architecture to collect real time biological data (or health information) from the patients to monitor and analyze the health conditions of the infected patients and generates alert messages that are transmitted to the concerned parties such as relatives, medical staff and doctors to provide appropriate treatment in a timely fashion. The health data are collected through sensors attached to the patients and transmitted to the edge devices and cloud storage for further processing. The collected data are analyzed through fuzzy logic in edge devices to efficiently identify the risk status (such as low risk, moderate risk and high risk) of the COVID19 patients in real time. The proposed system is also associated with a mobile app that enables the continuous monitoring of the health status of the patients. Moreover, once alerted by the system about the high risk status of a patient, a doctor can fetch all the health records of the patient for a specified period, which can be utilized for a detailed clinical diagnosis.

Enhanced Enzymatically Amplified Metallization on Nanostructured Surfaces for Multiplexed Point-of-Care Electrical Detection of COVID-19 Biomarkers

Inexpensive yet sensitive and specific biomarker detection is a critical bottleneck in diagnostics, monitoring, and surveillance of infectious diseases such as COVID-19. Multiplexed detection of several biomarkers can achieve wider diagnostic applicability, accuracy, and ease-of-use, while reducing cost. Current biomarker detection methods often use enzyme-linked immunosorbent assays (ELISA) with optical detection which offers high sensitivity and specificity. However, this is complex, expensive, and limited to detecting only a single analyte at a time. Here, it is found that biomarker-bound enzyme-labeled probes act synergistically with nanostructured catalytic surfaces and can be used to selectively reduce a soluble silver substrate to generate highly dense and conductive, localized surface silver metallization on microelectrode arrays. This enables a sensitive and quantitative, simple, direct electronic readout of biomarker binding without the use of any intermediate optics. Furthermore, the localized and dry-phase stable nature of the metallization enables multiplexed electronic measurement of several biomarkers from a single drop (<10 µL) of sample on a microchip.This method is applied for the multiplexed point-of-care (POC) quantitative detection of multiple COVID-19 antigen-specific antibodies. Combining a simple microchip and an inexpensive, cellphone-interfaced, portable reader, the detection and discrimination of biomarkers of prior infection versus vaccination is demonstrated.

A national surveillance of eosinophilic meningitis in Thailand

Introduction

Eosinophilic meningitis (EOM) is an emerging infectious disease worldwide. The most common cause of EOM is infection with Angiostrongylus cantonensis One possible method of monitoring and control of this infection is surveillance and prediction. There are limited data on national surveillance and predictive models on EOM. This study aimed to develop an online surveillance with a predictive model for EOM by using the national database.

Methods

We retrospectively retrieved reported cases of EOM from all provinces in Thailand and quantified them by month and year. Data were retrieved from Ministry of Public Health database. We developed a website application to explore the EOM cases in Thailand including regions and provinces using box plots. The website also provided the Autoregressive Integrated Moving Average (ARIMA) models and Seasonal ARIMA (SARIMA) models for predicting the disease cases from nation, region, and province levels. The suitable models were considered by minimum Akaike Information Criterion (AIC). The appropriate SARIMA model was used to predict the number of EOM cases.

Results

From 2003 to 2021, 3330 EOM cases were diagnosed and registered in the national database, with a peak in 2003 (median of 22 cases). We determined SARIMA(1,1,2)(2,0,0)[12] to be the most appropriate model, as it yielded the fitted values that were closest to the actual data. A predictive surveillance website was published on http://202.28.75.8/sample-apps/NationalEOM/.

Conclusions

We determined that web application can be used for monitoring and exploring the trend of EOM patients in Thailand. The predictive values matched the actual monthly numbers of EOM cases indicating a good fit of the predictive model.

Estimation of Japanese encephalitis virus infection prevalence in mosquitoes and bats through nationwide sentinel surveillance in Indonesia

Indonesia belongs to endemic areas of Japanese encephalitis (JE), yet data regarding the true risk of disease transmission are lacking. While many seroprevalence studies reported its classic enzootic transmission, data related to the role of bats in the transmission of JE virus are limited. This current study aimed to identify the potential role of bats in the local transmission of the JE virus to aid the ongoing active case surveillance in Indonesia, in order to estimate the transmission risk. Mosquitoes and bats were collected from 11 provinces in Indonesia. The detection of the JE virus used polymerase chain reaction (PCR). Maps were generated to analyze the JE virus distribution pattern. Logistic regression analysis was done to identify risk factors of JE virus transmission. JE virus was detected in 1.4% (7/483) of mosquito pools and in 2.0% (68/3,322) of bat samples. Mosquito species positive for JE virus were Culex tritaeniorhynchus and Cx. vishnui, whereas JE-positive bats belonged to the genera Cynopterus, Eonycteris, Hipposideros, Kerivoula, Macroglossus, Pipistrellus, Rousettus, Scotophilus and Thoopterus. JE-positive mosquitoes were collected at the same sites as the JE-positive bats. Collection site nearby human dwellings (AOR: 2.02; P = 0.009) and relative humidity of >80% (AOR: 2.40; P = 0.001) were identified as independent risk factors for JE virus transmission. The findings of the current study highlighted the likely ongoing risk of JE virus transmission in many provinces in Indonesia, and its potential implications on human health.

Role of artificial intelligence-internet of things (AI-IoT) based emerging technologies in the public health response to infectious diseases in Bangladesh

Digital technologies are the need of today to predict, prevent and control emerging infectious diseases. Bangladesh is one of the world's poorest and most densely populated countries and faces a double burden of two deadly diseases, COVID-19 and dengue. In response to both these diseases, the absence of a digital healthcare system and insufficient preparedness, lack of public awareness pose unique challenges and a large threat to the population, resulting in epidemics of escalating severity. This paper suggests a digital health care and surveillance system based on the internet of things (IoT) and artificial intelligence (AI) for timely identification of COVID-19 and dengue cases and improving the prevention and control strategies in the country.

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Digital technologies enable smart healthcare solutions to sustain and improve health services.

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Bangladesh is vulnerable to both COVID-19 and dengue epidemics.

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Epidemic preparedness requires improved digital health policy by integrating AI-IoT based emerging technologies.

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Campaigning to raise awareness of COVID-19 and dengue infections is a public health urgency.

A methodology for evaluating digital contact tracing apps based on the COVID-19 experience

Controlling the spreading of infectious diseases has been shown crucial in the COVID-19 pandemic. Traditional contact tracing is used to detect newly infected individuals by tracing their previous contacts, and by selectively checking and isolating any individuals likely to have been infected. Digital contact tracing with the utilisation of smartphones was contrived as a technological aid to improve this manual, slow and tedious process. Nevertheless, despite the high hopes raised when smartphone-based contact tracing apps were introduced as a measure to reduce the spread of the COVID-19, their efficiency has been moderately low. In this paper, we propose a methodology for evaluating digital contact tracing apps, based on an epidemic model, which will be used not only to evaluate the deployed Apps against the COVID-19 but also to determine how they can be improved for future pandemics. Firstly, the model confirms the moderate effectiveness of the deployed digital contact tracing, confirming the fact that it could not be used as the unique measure to fight against the COVID-19, and had to be combined with additional measures. Secondly, several improvements are proposed (and evaluated) to increase the efficiency of digital control tracing to become a more useful tool in the future.

Governance Quality, Public Health, Education, and Innovation: Study for Novel Implications

Pandemic or worldwide disease is the greatest issue of all time that not only affects human health but also influences the economic, educational, and other activities of the countries, since malaria is among the leading health disease that disrupts the economic system of the country. Therefore, this study aimed to analyze whether educational expenditure and technological innovation influence malarial incidence in emerging economies. This study also examined the role of government effectiveness, government health expenditure, gross domestic growth, human capital, and research and development during the period 2000-2018. Employing panel data approaches, including the slope heterogeneity and cross-sectional dependence, the second-generation unit root test reveals the stationarity of all variables. The study also validated the existence of a long-run relationship between the variables. Based on the asymmetrical distribution properties, this study employed the quantile regression approach. The empirical results asserted that education and technological innovation significantly reduce malarial incidents in the panel economies. Also, government effectiveness, research and development, and human capital adversely affect incidences of malaria. In contrast, gross domestic product is the only factor found that increases malarial incidents during the selected period. Based on the empirical results, this study suggested policy measures that could benefit the governors, policymakers, and scholars.

Home quarantine patient monitoring in the era of COVID-19 disease

Patients’ remote monitoring becomes even more crucial due to the spreading of the COVID-19 disease. Hospitals cannot accommodate all the patients who need to be taken care. Hence, tele-medicine or, as also named, tele-health, remains the only means available to keep the situation under control. In particular, it is important to monitor the patients who are subject to the home quarantine period. The reason is twofold: (i) their live status and symptoms must be controlled; (ii) they must not leave the permitted area during the quarantine period. To this end, the paper defines a set of rules and processes based on the Internet of Things (IoT) paradigm, which enable the integration of different devices, in order to monitor the required parameters related to the patient and notify life-threatening situations to the connected health-care structure. The conceived IoT network is developed by means of Node-RED, which is a flow-based programming tool targeted to the IoT. Particular attention is also paid to security and privacy requirements, since sensitive data related to the patients must be kept safe. The proposed solution is preliminary assessed by means of a test-bed.

Quality of National Disease Surveillance Reporting before and during COVID-19: A Mixed-Method Study in Indonesia

Background: Global COVID-19 outbreaks in early 2020 have burdened health workers, among them surveillance workers who have the responsibility to undertake routine disease surveillance activities. The aim of this study was to describe the quality of the implementation of Indonesia’s Early Warning and Response Alert System (EWARS) for disease surveillance and to measure the burden of disease surveillance reporting quality before and during the COVID-19 epidemic in Indonesia. Methods: A mixed-method approach was used. A total of 38 informants from regional health offices participated in Focus Group Discussion (FGD) and In-Depth Interview (IDI) for informants from Ministry of Health. The FGD and IDI were conducted using online video communication. Yearly completeness and timeliness of reporting of 34 provinces were collected from the application. Qualitative data were analyzed thematically, and quantitative data were analyzed descriptively. Results: Major implementation gaps were found in poorly distributed human resources and regional infrastructure inequity. National reporting from 2017–2019 showed an increasing trend of completeness (55%, 64%, and 75%, respectively) and timeliness (55%, 64%, and 75%, respectively). However, the quality of the reporting dropped to 53% and 34% in 2020 concomitant with the SARS-CoV2 epidemic. Conclusions: Report completeness and timeliness are likely related to regional infrastructure inequity and the COVID-19 epidemic. It is recommended to increase report capacities with an automatic EWARS application linked systems in hospitals and laboratories.

Analysis of bluetongue disease epizootics in sheep of Andhra Pradesh, India using spatial and temporal autocorrelation

Bluetongue (BT) disease poses a constant risk to the livestock population around the world. A better understanding of the risk factors will enable a more accurate prediction of the place and time of high-risk events. Mapping the disease epizootics over a period in a particular geographic area will identify the spatial distribution of disease occurrence. A Geographical Information System (GIS) based methodology to analyze the relationship between bluetongue epizootics and spatial-temporal patterns was used for the years 2000 to 2015 in sheep of Andhra Pradesh, India. Autocorrelation (ACF), partial autocorrelation (PACF), and cross-correlation (CCF) analyses were carried out to find the self-dependency between BT epizootics and their dependencies on environmental factors and livestock population. The association with climatic or remote sensing variables at different months lag, including wind speed, temperature, rainfall, relative humidity, normalized difference vegetation index (NDVI), normalized difference water index (NDWI), land surface temperature (LST), was also examined. The ACF & PACF of BT epizootics with its lag showed a significant positive autocorrelation with a month's lag (r = 0.41). Cross-correlations between the environmental variables and BT epizootics indicated the significant positive correlations at 0, 1, and 2 month's lag of rainfall, relative humidity, normalized difference water index (NDWI), and normalized difference vegetation index (NDVI). Spatial autocorrelation analysis estimated the univariate global Moran's I value of 0.21. Meanwhile, the local Moran's I value for the year 2000 (r = 0.32) showed a high degree of spatial autocorrelation. The spatial autocorrelation analysis revealed that the BT epizootics in sheep are having considerable spatial association among the outbreaks in nearby districts, and have to be taken care of while making any forecasting or disease prediction with other risk factors.

Artificial Intelligence Adoption in the Post COVID-19 New-Normal and Role of Smart Technologies in Transforming Business: a Review

Purpose

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

Design/methodology/approach

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

Findings

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research limitations/implications

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

Practical implications

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

Originality/value

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.

Covidseeker: A Geospatial Temporal Surveillance Tool

Introduction: Geospatial temporal data derived from smartphones traditionally used for purposes of navigation may offer valuable information for public health surveillance and locational hot spotting. Our objective was to develop a web-based application, called Covidseeker, that captures continuous fine-grained geospatial temporal data from smartphones and leverages these data to study transmission patterns of COVID-19. Methods: This report describes the development of Covidseeker and the process by which it utilizes geospatial temporal data from smartphones and processes it into a usable format to study geospatial temporal patterns of COVID-19. We provide an overview of the design process, the principles, the software architecture, and the dashboard of the Covidseeker application and consider key challenges and strategic uses of capturing geospatial temporal data and the potential for future applications in outbreak surveillance. Results: A resource such as Covidseeker can support situational awareness by providing information about the location and timing of transmission of diseases such as COVID-19. Geospatial temporal data housed in smartphones hold tremendous potential to capture more depth about where and when transmission occurs and the patterns of human mobility that lead to increases in risk of COVID-19. Conclusion: An enormous and highly rich source of geospatial temporal information about human mobility can be used to provide highly localized discrete information that is difficult to capture by traditional sources. The architecture of Covidseeker can be applied to help track COVID-19 and should be integrated with traditional disease surveillance practices.

Wearable body sensor network: SDGs panacea for an holistic SARS-CoV-2 mitigation, diagnostic, therapeutic, and health informatics interventions

The COVID-19 pandemic offers enormous potential for digital health solutions, with social distancing being the only current approach that has thus far been established to reduce the risk. Wearable Activity Tracker (WATs) is an appealing, successful, and inexpensive choice in this context. This technology is of vital significance in ensuring the well-being of both patients and healthcare professionals following sustainable development goals. The application of technological interventions will in no doubt help in tackling the debilitating effect of health emergencies in the world population. However, there is a shortage of literature explaining the programming resources needed to effectively treat this novel infection which is briefly addressed in this review. This study prominently addressed the potential of wearable Activity Tracker as a technological solution to healthcare disaster management.

FSS superstrate antenna for satellite cynosure on IoT to combat COVID-19 pandemic

The global pandemic, COVID-19 needs joint techniques and technology to combat it. The internet of things (IoT) has been at the forefront in solving problems, not only in the health care sector but in other sectors. It delivers accuracy with robustness in the developing service and application. However, it remains clear that the use of IoT is limited to coverage, longevity, security, connectivity issue, immediacy, and multicasting, we proposed in this paper frequency selective surface (FSS) as superstrate for rectangular microstrip antenna. An FSS design combine with the rectangular microstrip antenna for better performance is placed over FSS parallel configuration. The rectangular microstrip antenna was titled 45 degrees to change the band-stop. Analysis of the proposed performance in terms of gain, return loss, and directivity shows that the FSS structure's integration brings better results. With the help of a 3D electromagnetic computer simulation technology CST studio suite, we model the proposed antenna, perform the simulation with a frequency-domain solver, and validate it with a time-domain solver. The proposed impressive result is suitable for satellite networks, which hybrid with IoT can provide a sustainable long-time solution in fighting the COVID-19 pandemic.

Sample Preparation and Diagnostic Methods for a Variety of Settings: A Comprehensive Review

Sample preparation is an essential step for nearly every type of biochemical analysis in use today. Among the most important of these analyses is the diagnosis of diseases, since their treatment may rely greatly on time and, in the case of infectious diseases, containing their spread within a population to prevent outbreaks. To address this, many different methods have been developed for use in the wide variety of settings for which they are needed. In this work, we have reviewed the literature and report on a broad range of methods that have been developed in recent years and their applications to point-of-care (POC), high-throughput screening, and low-resource and traditional clinical settings for diagnosis, including some of those that were developed in response to the coronavirus disease 2019 (COVID-19) pandemic. In addition to covering alternative approaches and improvements to traditional sample preparation techniques such as extractions and separations, techniques that have been developed with focuses on integration with smart devices, laboratory automation, and biosensors are also discussed.

High real-time reporting of domestic and wild animal diseases following rollout of mobile phone reporting system in Kenya

BACKGROUND

To improve early detection of emerging infectious diseases in sub-Saharan Africa (SSA), many of them zoonotic, numerous electronic animal disease-reporting systems have been piloted but not implemented because of cost, lack of user friendliness, and data insecurity. In Kenya, we developed and rolled out an open-source mobile phone-based domestic and wild animal disease reporting system and collected data over two years to investigate its robustness and ability to track disease trends.

METHODS

The Kenya Animal Biosurveillance System (KABS) application was built on the Java® platform, freely downloadable for android compatible mobile phones, and supported by web-based account management, form editing and data monitoring. The application was integrated into the surveillance systems of Kenya's domestic and wild animal sectors by adopting their existing data collection tools, and targeting disease syndromes prioritized by national, regional and international animal and human health agencies. Smartphone-owning government and private domestic and wild animal health officers were recruited and trained on the application, and reports received and analyzed by Kenya Directorate of Veterinary Services. The KABS application performed automatic basic analyses (frequencies, spatial distribution), which were immediately relayed to reporting officers as feedback.

RESULTS

Of 697 trained domestic animal officers, 662 (95%) downloaded the application, and >72% of them started reporting using the application within three months. Introduction of the application resulted in 2- to 14-fold increase in number of disease reports when compared to the previous year (relative risk = 14, CI 13.8-14.2, p<0.001), and reports were more widely distributed. Among domestic animals, food animals (cattle, sheep, goats, camels, and chicken) accounted for >90% of the reports, with respiratory, gastrointestinal and skin diseases constituting >85% of the reports. Herbivore wildlife (zebra, buffalo, elephant, giraffe, antelopes) accounted for >60% of the wildlife disease reports, followed by carnivores (lions, cheetah, hyenas, jackals, and wild dogs). Deaths, traumatic injuries, and skin diseases were most reported in wildlife.

CONCLUSIONS

This open-source system was user friendly and secure, ideal for rolling out in other countries in SSA to improve disease reporting and enhance preparedness for epidemics of zoonotic diseases.

Management of the COVID-19 Pandemic in the Republic of Korea from the Perspective of Governance and Public-Private Partnership

The government of the Republic of Korea (ROK) has established relevant laws and a master plan for infectious disease control and prevention based on their experiences with past epidemics. In addition, the Ministry of Health and Welfare has designed a standard manual for risk management that involves pan-governmental crisis management systems. This national infectious disease management system is constantly being supplemented and developed in face of infectious disease-related crises. In this study, we set out to present directions for infectious disease prevention and flow of management and governance between central and local governments to ensure systematic quarantine activities in the ROK. During the coronavirus disease 2019 (COVID-19) pandemic, public-private partnerships have been established to collect, provide, process, and disseminate information for effective quarantine. This has enabled the development and rapid approval of test kits, the tracking of cases, and the allocation of appropriate resources for patient treatment. Additionally, the Infectious Disease Control Agency has actively utilized information and communication technology platforms to disclose information necessary in real-time for COVID-19 quarantine and management. Overall, these efforts have played an important role in epidemiological investigations, patient management, and quarantine in the early stages of the COVID-19 pandemic.

Exploring the Utility of Google Mobility Data During the COVID-19 Pandemic in India: Digital Epidemiological Analysis

BACKGROUND

Association between human mobility and disease transmission has been established for COVID-19, but quantifying the levels of mobility over large geographical areas is difficult. Google has released Community Mobility Reports (CMRs) containing data about the movement of people, collated from mobile devices.

OBJECTIVE

The aim of this study is to explore the use of CMRs to assess the role of mobility in spreading COVID-19 infection in India.

METHODS

In this ecological study, we analyzed CMRs to determine human mobility between March and October 2020. The data were compared for the phases before the lockdown (between March 14 and 25, 2020), during lockdown (March 25-June 7, 2020), and after the lockdown (June 8-October 15, 2020) with the reference periods (ie, January 3-February 6, 2020). Another data set depicting the burden of COVID-19 as per various disease severity indicators was derived from a crowdsourced API. The relationship between the two data sets was investigated using the Kendall tau correlation to depict the correlation between mobility and disease severity.

RESULTS

At the national level, mobility decreased from -38% to -77% for all areas but residential (which showed an increase of 24.6%) during the lockdown compared to the reference period. At the beginning of the unlock phase, the state of Sikkim (minimum cases: 7) with a -60% reduction in mobility depicted more mobility compared to -82% in Maharashtra (maximum cases: 1.59 million). Residential mobility was negatively correlated (-0.05 to -0.91) with all other measures of mobility. The magnitude of the correlations for intramobility indicators was comparatively low for the lockdown phase (correlation ≥0.5 for 12 indicators) compared to the other phases (correlation ≥0.5 for 45 and 18 indicators in the prelockdown and unlock phases, respectively). A high correlation coefficient between epidemiological and mobility indicators was observed for the lockdown and unlock phases compared to the prelockdown phase.

CONCLUSIONS

Mobile-based open-source mobility data can be used to assess the effectiveness of social distancing in mitigating disease spread. CMR data depicted an association between mobility and disease severity, and we suggest using this technique to supplement future COVID-19 surveillance.

PREDICTING COVID-19 SECOND WAVE SIGNAL IN SELECTED STATES OF SOUTHWEST NIGERIA: A COMPARISON OF CUMULATIVE SUM C2 AND CUMULATIVE SUM C1 EPIDEMIC THRESHOLDS

INTRODUCTION

Epidemic thresholds generated using the conventional historical data is not optimal for COVID-19 because of its short historical trajectory. This study therefore, aimed to develop and compare Cumulative sum C2 and C1 epidemic thresholds for COVID-19 in selected states in southwestern Nigeria.

METHODS

This was a retrospective longitudinal analysis of the COVID-19 surveillance data (week 10 - 48) retrieved from the Nigerian Centre for Disease Control (NCDC) database of situation reports as at the 6th of December, 2020. Data was managed with Microsoft excel. The weekly time scale was adopted for developing the CUSUM C2 and C1 epidemic thresholds for three selected southwest states and Nigeria.

RESULTS

A total of 236 situation reports were reviewed for each state. For Lagos state, the maximum C2 and C1 estimated was 2326 which was during the peak of the epidemic. From the four most recent surveillance points, the thresholds and the observed confirmed cases appeared to diverge from each other. For Ogun state, the maximum C2 and C1 estimated was 318 during the peak of the epidemic. From the four most recent surveillance points, the thresholds and the observed confirmed cases appeared to converge. For Oyo state, the maximum C2 and C1 estimated was 708 during the peak of the epidemic. From the four most recent surveillance points, the thresholds and the observed confirmed cases appeared to converge and then diverge.

CONCLUSION

A closer monitor of the surveillance data for the states is recommended for a possible public health intervention.

A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic's dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

Metagenomic sequencing reveals viral abundance and diversity in mosquitoes from the Shaanxi-Gansu-Ningxia region, China

BACKGROUND

Mosquitoes host and transmit numerous arthropod-borne viruses (arboviruses) that cause disease in both humans and animals. Effective surveillance of virome profiles in mosquitoes is vital to the prevention and control of mosquito-borne diseases in northwestern China, where epidemics occur frequently.

METHODS

Mosquitoes were collected in the Shaanxi-Gansu-Ningxia region (Shaanxi Province, Gansu Province, and Ningxia Hui Autonomous Region) of China from June to August 2019. Morphological methods were used for taxonomic identification of mosquito species. High-throughput sequencing and metagenomic analysis were used to characterize mosquito viromes.

RESULTS

A total of 22,959 mosquitoes were collected, including Culex pipiens (45.7%), Culex tritaeniorhynchus (40.6%), Anopheles sinensis (8.4%), Aedes (5.2%), and Armigeres subalbatus (0.1%). In total, 3,014,183 (0.95% of clean reads) viral sequences were identified and assigned to 116 viral species (including pathogens such as Japanese encephalitis virus and Getah virus) in 31 viral families, including Flaviviridae, Togaviridae, Phasmaviridae, Phenuiviridae, and some unclassified viruses. Mosquitoes collected in July (86 species in 26 families) showed greater viral diversity than those from June and August. Culex pipiens (69 species in 25 families) and Culex tritaeniorhynchus (73 species in 24 families) carried more viral species than Anopheles sinensis (50 species in 19 families) or Aedes (38 species in 20 families) mosquitoes.

CONCLUSION

Viral diversity and abundance were affected by mosquito species and collection time. The present study elucidates the virome compositions of various mosquito species in northwestern China, improving the understanding of virus transmission dynamics for comparison with those of disease outbreaks.

A remote management system for control and surveillance of echinococcosis: design and implementation based on internet of things

Background

As a neglected cross-species parasitic disease transmitted between canines and livestock, echinococcosis remains a global public health concern with a heavy disease burden. In China, especially in the epidemic pastoral communities on the Qinghai-Tibet Plateau, the harsh climate, low socio-economic status, poor overall hygiene, and remote and insufficient access to all owned dogs exacerbate the difficulty in implementing the ambitious control programme for echinococcosis. We aimed to design and implement a remote management system (RMS) based on internet of things (IoT) for control and surveillance of echinococcosis by combining deworming devices to realise long-distance smart deworming control, smooth statistical analysis and result display. New methods and tools are urgently needed to increase the deworming coverage and frequency, promote real-time scientific surveillance, and prevent transmission of echinococcosis in remoted transmission areas.

Methods

From 2016 to 2019, we had cooperated and developed the smart collar and smart feeder with the Central Research Institute of Shanghai Electric Group Co., Ltd. (Shanghai, China) and Shenzhen Jizhi Future Technology Co., Ltd. (Shenzhen, China). From September 2019 to March 2020, We had proposed the RMS based on IoT as a novel tool to control smart deworming devices to deliver efficient praziquantel (PZQ) baits to dogs regularly and automatically and also as a smart digital management platform to monitor, analyse, and display the epidemic trends of echinococcosis dynamically, in real time in Hezuo City, Gannan Tibetan Autonomous Prefecture, Gansu Province, China. Starting from January 2018, The RMS has been maintained and upgraded by Shanghai Yier Information Technology Co., Ltd (Shanghai, China). The database was based on MySQL tools and the Chi-square test was used to probe the difference and changes of variables in different groups.

Results

The smart collars are fully capable of anti-collision, waterproof, and cold-proof performance, and the battery’s energy is sufficient, the anti-collision rate, water-proof rate, cold-proof rate and voltage normal rate is 99.6% (521/523), 100.0% (523/523), 100.0% (523/523) and 100.0% (523/523), respectively. The RMS can accurately analyse the monitoring data and parameters including positive rates of canine faeces, and the prevalence of echinococcosis in the general population livestock, and children. The data of dogs deworming and surveillance for echinococcosis is able to be controlled using RMS and has expanded gradually in townships to the whole Hezuo region. The automatic delivering PZQ rate, collar positioning rate, deliver PZQ reminding rate, and fault report rate is 91.1% (1914/2102), 92.1% (13 580/14 745), 92.1% (1936/2102) and 84.7% (1287/1519), respectively. After using the RMS from 2019, the missing rate of monitoring data decreased from 32.1% (9/28) to 0 (0/16). A total of 48 administrators (3, 3, 8, 11, 23 at the provincial, municipal, county, township, village levels, respectively) participated in the questionnaire survey, with 93.8% of its overall satisfaction rate.

Conclusions

The existing difficulties and challenges in the way of prevention and control for echinococcosis can partially be resolved using the innovative, IoT-based technologies and tools. The proposed RMS advance the upgrade of existing manual prevention and control models for echinococcosis, especially in the current ongoing COVID-19 pandemic, as social distance and community blockade continue.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-021-00833-4.

Evaluation of the number of undiagnosed infected in an outbreak using source of infection measurements

In times of outbreaks, an essential requirement for better monitoring is the evaluation of the number of undiagnosed infected individuals. An accurate estimate of this fraction is crucial for the assessment of the situation and the establishment of protective measures. In most current studies using epidemics models, the total number of infected is either approximated by the number of diagnosed individuals or is dependent on the model parameters and assumptions, which are often debated. We here study the relationship between the fraction of diagnosed infected out of all infected, and the fraction of infected with known contaminator out of all diagnosed infected. We show that those two are approximately the same in exponential models and across most models currently used in the study of epidemics, independently of the model parameters. As an application, we compute an estimate of the effective number of infected by the SARS-CoV-2 virus in various countries.

Suggesting a framework for preparedness against the pandemic outbreak based on medical informatics solutions: a thematic analysis

Background

When an outbreak emerged, each country needs a coherent and preventive plan to deal with epidemics. In the era of technology, adopting informatics‐based solutions is essential. The main objective of this study is to propose a conceptual framework to provide a rapid and responsive surveillance system against pandemics.

Methods

A three‐step approach was employed in this research to develop a conceptual framework. These three steps comprise (1) literature review, (2) extracting and coding concepts, and determining main themes based on thematic analysis using ATLAS.ti® software, and (3) mapping concepts. Later, all of the results synthesized under expert consultation to design a conceptual framework based on the main themes and identified strategies related to medical informatics.

Results

In the literature review phase, 65 articles were identified as eligible studies for analysis. Through line by line coding in thematic analysis, more than 46 themes were extracted as potential foremost themes. Based on the key themes and strategies were employed by studies, the proposed framework designed in three main components. The most appropriate strategies that can be used in each section were identified based on the demands of each part and the available solutions. These solutions were employed in the final framework.

Conclusion

The presented model in this study can be the first step for a better understanding of the potential of medical informatics solutions in promoting epidemic disease management. It can be applied as a reference model for designing intelligent surveillance systems to prepare for probable future pandemics.

Predicting epidemics using search engine data: a comparative study on measles in the largest countries of Europe

BACKGROUND

In recent years new forms of syndromic surveillance that use data from the Internet have been proposed. These have been developed to assist the early prediction of epidemics in various cases and diseases. It has been found that these systems are accurate in monitoring and predicting outbreaks before these are observed in population and, therefore, they can be used as a complement to other methods. In this research, our aim is to examine a highly infectious disease, measles, as there is no extensive literature on forecasting measles using Internet data, METHODS: This research has been conducted with official data on measles for 5 years (2013-2018) from the competent authority of the European Union (European Center of Disease and Prevention - ECDC) and data obtained from Google Trends by using scripts coded in Python. We compared regression models forecasting the development of measles in the five countries.

RESULTS

Results show that measles can be estimated and predicted through Google Trends in terms of time, volume and the overall spread. The combined results reveal a strong relationship of measles cases with the predicted cases (correlation coefficient R= 0.779 in two-tailed significance p< 0.01). The mean standard error was relatively low 45.2 (12.19%) for the combined results. However, major differences and deviations were observed for countries with a relatively low impact of measles, such as the United Kingdom and Spain. For these countries, alternative models were tested in an attempt to improve the results.

CONCLUSIONS

The estimation of measles cases from Google Trends produces acceptable results and can help predict outbreaks in a robust and sound manner, at least 2 months in advance. Python scripts can be used individually or within the framework of an integrated Internet surveillance system for tracking epidemics as the one addressed here.

IoMT amid COVID-19 pandemic: Application, architecture, technology, and security

In many countries, the Internet of Medical Things (IoMT) has been deployed in tandem with other strategies to curb the spread of COVID-19, improve the safety of front-line personnel, increase efficacy by lessening the severity of the disease on human lives, and decrease mortality rates. Significant inroads have been achieved in terms of applications and technology, as well as security which have also been magnified through the rapid and widespread adoption of IoMT across the globe. A number of on-going researches show the adoption of secure IoMT applications is possible by incorporating security measures with the technology. Furthermore, the development of new IoMT technologies merge with Artificial Intelligence, Big Data and Blockchain offers more viable solutions. Hence, this paper highlights the IoMT architecture, applications, technologies, and security developments that have been made with respect to IoMT in combating COVID-19. Additionally, this paper provides useful insights into specific IoMT architecture models, emerging IoMT applications, IoMT security measurements, and technology direction that apply to many IoMT systems within the medical environment to combat COVID-19.

Spatio-temporal and time series analysis of bluetongue outbreaks with environmental factors extracted from Google Earth Engine (GEE) in Andhra Pradesh, India

This study describes the spatial and temporal patterns of bluetongue (BT) outbreaks with environmental factors in undivided Andhra Pradesh, India. Descriptive analysis of the reported BT outbreaks (n = 2,697) in the study period (2000-2017) revealed a higher frequency of outbreaks during monsoon and post-monsoon months. Correlation analysis of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), rainfall and relative humidity (RH) displayed a significant positive correlation with BT outbreaks (p < .05). Retrospective unadjusted space-time, adjusted temporal and spatial analysis detected two, five and two statistically significant (p < .05) clusters, respectively. Time series distribution lag analysis examined the temporal patterns of BT outbreaks with environmental, biophysical factors and estimated that a decrease in 1 unit of rainfall (mm) was associated with 0.2% increase in the outbreak at lag 12 months. Similarly, a 1°C increase in land surface temperature (LST) was associated with 6.54% increase in the outbreaks at lag 12 months. However, an increase in 1 unit of wind speed (m/s) was associated with a 16% decrease in the outbreak at lag 10 months. The predictive model indicated that the peak of BT outbreaks were from October to December, the post-monsoon season in Andhra Pradesh region. The findings suggest that environmental factors influence BT outbreaks, and due to changes in climatic conditions, we may notice higher numbers of BT outbreaks in the coming years. The knowledge of spatial and temporal clustering of BT outbreaks may assist in adopting proper measures to prevent and control the BT spread.

Next-generation computational tools and resources for coronavirus research: From detection to vaccine discovery

The COVID-19 pandemic has affected 215 countries and territories around the world with 60,187,347 coronavirus cases and 17,125,719 currently infected patients confirmed as of the November 25, 2020. Currently, many countries are working on developing new vaccines and therapeutic drugs for this novel virus strain, and a few of them are in different phases of clinical trials. The advancement in high-throughput sequence technologies, along with the application of bioinformatics, offers invaluable knowledge on genomic characterization and molecular pathogenesis of coronaviruses. Recent multi-disciplinary studies using bioinformatics methods like sequence-similarity, phylogenomic, and computational structural biology have provided an in-depth understanding of the molecular and biochemical basis of infection, atomic-level recognition of the viral-host receptor interaction, functional annotation of important viral proteins, and evolutionary divergence across different strains. Additionally, various modern immunoinformatic approaches are also being used to target the most promiscuous antigenic epitopes from the SARS-CoV-2 proteome for accelerating the vaccine development process. In this review, we summarize various important computational tools and databases available for systematic sequence-structural study on coronaviruses. The features of these public resources have been comprehensively discussed, which may help experimental biologists with predictive insights useful for ongoing research efforts to find therapeutics against the infectious COVID-19 disease.

How Human Mobility Models Can Help to Deal with COVID-19

One of the key factors for the spreading of human infections, such as the COVID-19, is human mobility. There is a huge background of human mobility models developed with the aim of evaluating the performance of mobile computer networks, such as cellular networks, opportunistic networks, etc. In this paper, we propose the use of these models for evaluating the temporal and spatial risk of transmission of the COVID-19 disease. First, we study both pure synthetic model and simulated models based on pedestrian simulators, generated for real urban scenarios such as a square and a subway station. In order to evaluate the risk, two different risks of exposure are defined. The results show that we can obtain not only the temporal risk but also a heat map with the exposure risk in the evaluated scenario. This is particularly interesting for public spaces, where health authorities could make effective risk management plans to reduce the risk of transmission.

Testing the identification effectiveness of an unknown outbreak of the Infectious Diseases Seeker (IDS) using and comparing the novel coronavirus disease (COVID-19) outbreak with the past SARS and MERS epidemics

Background

The aim of this research is to assess the predictive accuracy of the Infectious Diseases Seeker (IDS) – an innovative tool for prompt identification of the causative agent of infectious diseases during outbreaks – when field epidemiological data collected from a novel outbreak of unknown origin are analysed by the tool. For this reason, it has been taken into account the novel coronavirus disease (COVID-19) outbreak, which began in China at the end of December 2019, has rapidly spread around the globe, and it has led to a public health emergency of international concern (PHEIC), declared to the 30th of January 2020 by the World Health Organization (WHO).

Methods

The IDS takes advantage of an off-line database, built before the COVID-19 pandemic, which represents a pivotal characteristic for working without an internet connection. The software has been tested using the epidemiological data available in different and progressive stages of the COVID-19 outbreak. As a comparison, the results of the tests performed using the epidemiological data from the Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) epidemic in 2002 and Middle East Respiratory Syndrome coronavirus (MERS-CoV) epidemic in 2012, are shown.

Results

The overall outcomes provided by the software are comforting, as a matter of the fact that IDS has identified with a good accuracy the SARS and MERS epidemics (over 90%), while, as expected, it has not provided erroneous and equivocal readings after the elaboration COVID-19 epidemic data.

Conclusions

Even though IDS has not recognized the COVID-19 epidemic, it has not given to the end user a false result and wrong interpretation, as expected by the developers. For this reason, IDS reveals itself as useful software to identify a possible epidemic or outbreak. Thus, the intention of developers is to plan, once the software will be released, dedicated updates and upgrades of the database (e.g., SARS-CoV-2) in order to keep this tool increasingly useful and applicable to reality.

High-Level Design and Rapid Implementation of a Clinical and Non-clinical Blockchain-Based Data Sharing Platform for COVID-19 Containment

As the world has moved toward rigorous containment measures due to the spread of a novel coronavirus, it is crucial to push the boundaries of clinical data obtaining methods using real-time capturing facilities. During this time of crisis, data-centric technologies that could provide authenticity and immediate access to patient data are essential. A blockchain-based digital health protocol for access to real-time data with user-centric data protection measures can achieve these aims. Immediate and secure access to biomedical data can provide credible insights and also help in discovering intelligence to expediate the development of effective therapeutics. It also aids in altering policies for restrictions by extracting key insights required for modeling studies. This paper delivers a high-level design of a blockchain-based clinical research data collection and health service intervention platform, where the users can exercise control of data. This application also provides a platform to deliver technology-based interventions which would assist in streamlining aid for vulnerable users to prevent the NHS from being overwhelmed. Further steps are also recommended to achieve a data compliant solution for rapid deployment, based on available resources, allowing a collaborative effort, which is extremely necessary at times of such crisis.

Information technology in emergency management of COVID-19 outbreak

Emergency management of the emerging infectious disease outbreak is critical for public health threats. Currently, control of the COVID-19 outbreak is an international concern and has become a crucial challenge in many countries. This article reviews significant information technologyIT) applications in emergency management of COVID-19 by considering the prevention/mitigation, preparedness, response, and recovery phases of the crisis. This review was conducted using MEDLINE PubMed), Embase, IEEE, and Google Scholar. Expert opinions were collected to show existence gaps, useful technologies for each phase of emergency management, and future direction. Results indicated that various IT-based systems such as surveillance systems, artificial intelligence, computational methods, Internet of things, remote sensing sensor, online service, and GIS geographic information system) could have different outbreak management applications, especially in response phases. Information technology was applied in several aspects, such as increasing the accuracy of diagnosis, early detection, ensuring healthcare providers' safety, decreasing workload, saving time and cost, and drug discovery. We categorized these applications into four core topics, including diagnosis and prediction, treatment, protection, and management goals, which were confirmed by five experts. Without applying IT, the control and management of the crisis could be difficult on a large scale. For reducing and improving the hazard effect of disaster situations, the role of IT is inevitable. In addition to the response phase, communities should be considered to use IT capabilities in prevention, preparedness, and recovery phases. It is expected that IT will have an influential role in the recovery phase of COVID-19. Providing IT infrastructure and financial support by the governments should be more considered in facilitating IT capabilities.

Internet of Things for Current COVID-19 and Future Pandemics: an Exploratory Study

In recent years, the Internet of Things (IoT) has gained convincing research ground as a new research topic in a wide variety of academic and industrial disciplines, especially in healthcare. The IoT revolution is reshaping modern healthcare systems by incorporating technological, economic, and social prospects. It is evolving healthcare systems from conventional to more personalized healthcare systems through which patients can be diagnosed, treated, and monitored more easily. The current global challenge of the pandemic caused by the novel severe respiratory syndrome coronavirus 2 presents the greatest global public health crisis since the pandemic influenza outbreak of 1918. At the time this paper was written, the number of diagnosed COVID-19 cases around the world had reached more than 31 million. Since the pandemic started, there has been a rapid effort in different research communities to exploit a wide variety of technologies to combat this worldwide threat, and IoT technology is one of the pioneers in this area. In the context of COVID-19, IoT-enabled/linked devices/applications are utilized to lower the possible spread of COVID-19 to others by early diagnosis, monitoring patients, and practicing defined protocols after patient recovery. This paper surveys the role of IoT-based technologies in COVID-19 and reviews the state-of-the-art architectures, platforms, applications, and industrial IoT-based solutions combating COVID-19 in three main phases, including early diagnosis, quarantine time, and after recovery.

Evaluating the Effectiveness of COVID-19 Bluetooth-Based Smartphone Contact Tracing Applications

One of the strategies to control the spread of infectious diseases is based on the use of specialized applications for smartphones. These apps offer the possibility, once individuals are detected to be infected, to trace their previous contacts in order to test and detect new possibly-infected individuals. This paper evaluates the effectiveness of recently developed contact tracing smartphone applications for COVID-19 that rely on Bluetooth to detect contacts. We study how these applications work in order to model the main aspects that can affect their performance: precision, utilization, tracing speed and implementation model (centralized vs. decentralized). Then, we propose an epidemic model to evaluate their efficiency in terms of controlling future outbreaks and the effort required (e.g., individuals quarantined). Our results show that smartphone contact tracing can only be effective when combined with other mild measures that can slightly reduce the reproductive number R0 (for example, social distancing). Furthermore, we have found that a centralized model is much more effective, requiring an application utilization percentage of about 50% to control an outbreak. On the contrary, a decentralized model would require a higher utilization to be effective.

Human-Animal Interaction and the Emergence of SARS-CoV-2

The COVID-19 pandemic has affected all sectors of society, from health and economics to socialization and travel. The level and extent of this impact are unprecedented. Although the cause of COVID-19 was quickly identified to be a new coronavirus (SARS-CoV-2), the world was poorly prepared for preventing its spread. One important pillar of preparedness is surveillance of the sources of emerging pathogens and responding appropriately to prevent their spread in the human population. The ever-increasing interaction between humans and animals is one leading factor in facilitating the emergence of new pathogens. In this viewpoint, we discuss the possibility of the zoonotic origin of SARS-CoV-2, highlight the importance of understanding human-animal interaction to improve preparedness for future outbreaks, and outline recommendations for prevention.

Identifying the outbreak signal of COVID-19 before the response of the traditional disease monitoring system

SYNOPSIS

Early identification of the emergence of an outbreak of a novel infectious disease is critical to generating a timely response. The traditional monitoring system is adequate for detecting the outbreak of common diseases; however, it is insufficient for the discovery of novel infectious diseases. In this study, we used COVID-19 as an example to compare the delay time of different tools for identifying disease outbreaks. The results showed that both the abnormal spike in influenza-like illnesses and the peak of online searches of key terms could provide early signals. We emphasize the importance of testing these findings and discussing the broader potential to use syndromic surveillance, internet searches, and social media data together with traditional disease surveillance systems for early detection and understanding of novel emerging infectious diseases.