Data science approaches to infectious disease surveillance

Zhang, Qingpeng

doi:10.1098/rsta.2021.0115

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…Some mutations at critical locations of the genetic sequence may change the virus’s transmissibility and severity. Here we extend the multiple-strain model 35 , 36 , 41 to characterize viral evolutionary dynamics. For country i , such dynamics are captured by the transmissibility matrix , the severity matrix and the mutation matrix , all with dimensions M × M .…”

Section: Methodsmentioning

confidence: 99%

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Equitable access to COVID-19 vaccines makes a life-saving difference to all countries

et al. 2022

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Despite broad agreement on the negative consequences of vaccine inequity, the distribution of COVID-19 vaccines is imbalanced. Access to vaccines in high-income countries (HICs) is far greater than in low- and middle-income countries (LMICs). As a result, there continue to be high rates of COVID-19 infections and deaths in LMICs. In addition, recent mutant COVID-19 outbreaks may counteract advances in epidemic control and economic recovery in HICs. To explore the consequences of vaccine (in)equity in the face of evolving COVID-19 strains, we examine vaccine allocation strategies using a multistrain metapopulation model. Our results show that vaccine inequity provides only limited and short-term benefits to HICs. Sharper disparities in vaccine allocation between HICs and LMICs lead to earlier and larger outbreaks of new waves. Equitable vaccine allocation strategies, in contrast, substantially curb the spread of new strains. For HICs, making immediate and generous vaccine donations to LMICs is a practical pathway to protect everyone.

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Section: Methodsmentioning

confidence: 99%

“…, M }, respectively. We set a country-specific severity matrix to account for the heterogeneity in the health-care burden of COVID-19 and the age structure in different countries (Supplementary Note 3 ) 41 , 49 , 50 . We assume a linear strain space and local movement by a one-direction stepwise mutation in the model 47 .…”

Section: Methodsmentioning

confidence: 99%

Equitable access to COVID-19 vaccines makes a life-saving difference to all countries

et al. 2022

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“…During the outbreak of the COVID-19 pandemic, predictive data science techniques using real-time data were very fundamental in both disease surveillance, digital contact tracing, diagnosis, predicting the magnitude and impact of expanded outbreak. Data scientists have shown that by joining with medicine and public health scholars they can identify, analyze and model traditional and novel data generated by, or associated with, the pandemic to produce rich understandings [41]. These similar approaches are very relevant to monitoring, evaluation and learning because of the opportunity they present to have access to timely data, use of data of good quality and the power of data science techniques to quickly generate the required insights for decision making or making adaptations.…”

Section: Recommendationsmentioning

confidence: 99%

A data–driven model to predict a household’s capacity to graduate. Case ofUSAID/Twiyubake Program

Muramira

Nkurunziza

2022

Preprint

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The USAID funded Twiyubake program applied a dual systems and family-centered approach to provide a comprehensive package of services that aimed to strengthen the capacity and resilience of the targeted vulnerable families/households. The program built upon and strengthened existing community care structures to deliver and sustain high quality services to the enrolled households including economic strengthening, health promotion, water sanitation and hygiene etc. The Household Vulnerability and Graduation Assessment was used by Twiyubake program to determine household capacity to graduate from direct program assistance. The dataset used in this study came from the Twiyubake program’s annual monitoring data. Five different machine learning classification models namely Logistic Regression, Random Forest, Gaussian Naive Bayes, K-Nearest Neighbors, and Multi-Layer Perceptron, were used to build a predictive model of household capacity to graduate. The performance of predictive models was assessed using a variety of metrics, including the confusion matrix, accuracy, precision, recall, F1 score, and the Area under the Receiver Operating Characteristics. The results showed that the Random Forest model correctly predicted household graduation capacity with 94.8 percent of the accuracy, with recall score of 94.8 percent, precision score of 94.8 percent, and F1 score of 94.8 percent. Overall, all the machine learning algorithms performed very well with over 85 percent performance on the different evaluation parameters used. The Random Forest model was recommended as the best household graduation prediction classifier for the Twiyubake program since it performed better than the rest.

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“…Variable transformation provides an approach in solving problems such as distribution and linear characteristics [20]. Owing to the progress in multiple disciplines, including computer science, data science, applied mathematics, and statistics, we have moved closer to innovative data science approaches to obtain significant viewpoints in disease incidence and management [21][22][23][24]. There have been studies that identified the contribution of variable transformation in applications.…”

Section: Introductionmentioning

confidence: 99%

An Analytical Approach for Temporal Infection Mapping and Composite Index Development

Wang,

Weng,

Zhu

et al. 2023

Mathematics

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Significant and composite indices for infectious disease can have implications for developing interventions and public health. This paper presents an investment for developing access to further analysis of the incidence of individual and multiple diseases. This research mainly comprises two steps: first, an automatic and reproducible procedure based on functional data analysis techniques was proposed for analyzing the dynamic properties of each disease; second, orthogonal transformation was adopted for the development of composite indices. Between 2000 and 2019, nineteen class B notifiable diseases in China were collected for this study from the National Bureau of Statistics of China. The study facilitates the probing of underlying information about the dynamics from discrete incidence rates of each disease through the procedure, and it is also possible to obtain similarities and differences about diseases in detail by combining the derivative features. There has been great success in intervening in the majority of notifiable diseases in China, like bacterial or amebic dysentery and epidemic cerebrospinal meningitis, while more efforts are required for some diseases, like AIDS and virus hepatitis. The composite indices were able to reflect a more complex concept by combining individual incidences into a single value, providing a simultaneous reflection for multiple objects, and facilitating disease comparisons accordingly. For the notifiable diseases included in this study, there was superior management of gastro-intestinal infectious diseases and respiratory infectious diseases from the perspective of composite indices. This study developed a methodology for exploring the prevalent properties of infectious diseases. The development of effective and reliable analytical methods provides special insight into infectious diseases’ common dynamics and properties and has implications for the effective intervention of infectious diseases.

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Data science approaches to infectious disease surveillance

Cited by 10 publications

References 15 publications

Equitable access to COVID-19 vaccines makes a life-saving difference to all countries

Equitable access to COVID-19 vaccines makes a life-saving difference to all countries

A data–driven model to predict a household’s capacity to graduate. Case ofUSAID/Twiyubake Program

An Analytical Approach for Temporal Infection Mapping and Composite Index Development

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