Nowcasting Influenza-like Illness Trends in Cameroon

Nsoesie, Elaine O.; Oladeji, Olubusola; Abah, Aristide S. Abah; Ndeffo-Mbah, Martial L.

doi:10.1101/2020.07.02.20145250

Cited by 3 publications

(3 citation statements)

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“…This reinforces the need for developing contextualized approaches that take into account cultural and social practices. 18 Lastly, geographical variability in our estimates suggest that factors as Internet usage, representation and socioeconomic factors should be considered when using digital data for disease surveillance.…”

Section: Discussionmentioning

confidence: 95%

“…While there are indications that these data could be useful for supplementing traditional surveillance systems, few studies have focused on countries in Africa. 17,18 Specifically, searches of disease-related terms (such as, COVID-19) could reveal trends in cases. A recent report estimated that 56.2% of the South African population had access to the internet in 2017.…”

Section: Introduction (1473 Words)mentioning

confidence: 99%

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Nowcasting and forecasting provincial-level SARS-CoV-2 case positivity using google search data in South Africa

Nsoesie

Oladeji

et al. 2020

Preprint

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Data from non-traditional data sources, such as social media, search engines, and remote sensing, have previously demonstrated utility for disease surveillance. Few studies, however, have focused on countries in Africa, particularly during the SARS-CoV-2 pandemic. In this study, we use searches of COVID-19 symptoms, questions, and at-home remedies submitted to Google to model COVID-19 in South Africa, and assess how well the Google search data forecast short-term COVID-19 trends. Our findings suggest that information seeking trends on COVID-19 could guide models for anticipating COVID-19 trends and coordinating appropriate response measures.

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Section: Discussionmentioning

confidence: 95%

Section: Introduction (1473 Words)mentioning

confidence: 99%

Nowcasting and forecasting provincial-level SARS-CoV-2 case positivity using google search data in South Africa

Nsoesie

Oladeji

et al. 2020

Preprint

Self Cite

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“…Of the 45 indicators, 18 were found to be "Highly likely" suitable for nowcasting, seven were found to be "Likely", and 20 were found to be "Unlikely". Explanatory variables for a nowcasting model around health indicators are likely to exist, given the extent to which they are already modelled and forecasted [25][26][27][28][29]. The primary reason for a Goal 3 indicator to get a label of "Unlikely" suitable for nowcasting was data availability.…”

Section: Goal 3: Good Health and Well-beingmentioning

confidence: 99%

Feasibility of nowcasting SDG indicators: A comprehensive survey

Hopp¹,

Fu²,

Peltola³

2022

SJI

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The 2030 Agenda and accompanying Sustainable Development Goals (SDGs) are vital in guiding national and global policy. However, many of the SDG indicators used to measure progress toward those goals suffer from long publication lags. Nowcasting has the potential to address this problem and generate more timely estimates of those indicators. This paper provides resources for achieving that potential by 1) carrying out a comprehensive nowcasting feasibility survey of all SDG indicators to assess their potential to be nowcast, and 2) performing a case study of indicator 9.4.1 to illustrate and shed light on the process of performing a nowcasting exercise. There exist 231 SDG indicators, but due to only examining Tier 1 indicators and the fact that many indicators have multiple sub-indicators, 362 indicators and sub-indicators were eventually surveyed. Of those 362, 150 were found highly likely to be suitable candidates for nowcasting, 87 were found to be likely, and 125 were found to be unsuitable.

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Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study (Preprint)

Oladeji¹,

Zhang²,

Moradi³

et al. 2020

Preprint

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BACKGROUND The prevalence of chronic conditions such as obesity, hypertension, and diabetes is increasing in African countries. Many chronic diseases have been linked to risk factors such as poor diet and physical inactivity. Data for these behavioral risk factors are usually obtained from surveys, which can be delayed by years. Behavioral data from digital sources, including social media and search engines, could be used for timely monitoring of behavioral risk factors. OBJECTIVE The objective of our study was to propose the use of digital data from internet sources for monitoring changes in behavioral risk factors in Africa. METHODS We obtained the adjusted volume of search queries submitted to Google for 108 terms related to diet, exercise, and disease from 2010 to 2016. We also obtained the obesity and overweight prevalence for 52 African countries from the World Health Organization (WHO) for the same period. Machine learning algorithms (ie, random forest, support vector machine, Bayes generalized linear model, gradient boosting, and an ensemble of the individual methods) were used to identify search terms and patterns that correlate with changes in obesity and overweight prevalence across Africa. Out-of-sample predictions were used to assess and validate the model performance. RESULTS The study included 52 African countries. In 2016, the WHO reported an overweight prevalence ranging from 20.9% (95% credible interval [CI] 17.1%-25.0%) to 66.8% (95% CI 62.4%-71.0%) and an obesity prevalence ranging from 4.5% (95% CI 2.9%-6.5%) to 32.5% (95% CI 27.2%-38.1%) in Africa. The highest obesity and overweight prevalence were noted in the northern and southern regions. Google searches for diet-, exercise-, and obesity-related terms explained 97.3% (root-mean-square error [RMSE] 1.15) of the variation in obesity prevalence across all 52 countries. Similarly, the search data explained 96.6% (RMSE 2.26) of the variation in the overweight prevalence. The search terms yoga, exercise, and gym were most correlated with changes in obesity and overweight prevalence in countries with the highest prevalence. CONCLUSIONS Information-seeking patterns for diet- and exercise-related terms could indicate changes in attitudes toward and engagement in risk factors or healthy behaviors. These trends could capture population changes in risk factor prevalence, inform digital and physical interventions, and supplement official data from surveys.

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Nowcasting Influenza-like Illness Trends in Cameroon

Cited by 3 publications

References 44 publications

Nowcasting and forecasting provincial-level SARS-CoV-2 case positivity using google search data in South Africa

Nowcasting and forecasting provincial-level SARS-CoV-2 case positivity using google search data in South Africa

Feasibility of nowcasting SDG indicators: A comprehensive survey

Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study (Preprint)

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