Utilization of Mobile Phone Data Analysis: Pandemic Epidemiology

Prof. K.H. Grantz et al., and his collaboration research team at various universities from USA have been reviewed on the utilization of mobile phone data analysis in this COVID-19 pandemic epidemiology. This research review was reported in Nature Communications journal published on 30^th September 2020.

Usage of mobile phone data analysis should be considered with a careful understanding of the present populations with their behavior is an outbreak response in this pandemic epidemiology. It was proposed to assess the potential drivers with a spatiotemporal spread and also to monitor the effectiveness of non-pharmaceutical interventions (NPIs) to support the contact tracing efforts. Further, they have reviewed various applications used for mobile phone data analysis with proper guidance and evaluation of COVID-19 response in this pandemic epidemiology. Also with the relevance of these various applications for infectious disease with transmission control for the potential sources and implications of selection bias in mobile phone data. For balancing the social and economic costs, the public health response is seeking effectively to mitigate this pandemic condition. Interpretation of the data analysis for public health decision making was discussed in detail [1-3].

Mobile phone data has become one of the best information sources for the large-scale population. It can be seen that there has been increasing interest and significant attention has grown towards for the development of innovative methods and tools. These methods are mainly employed for the public health response to inform via mobile phone data that includes digital data, which can be collected passively through mobile phone operators and actively collected via recently developed applications. Hence, these data can be collected in high and low-income settings that can make changes in mobility and clustering patterns, to capture, in the near real-time analysis [4].

Utilization of mobile phone data during pandemic response:

Utilization of mobile phone data is to inform various aspects during the response in this pandemic condition. It can be seen that at the individual level, the mobile phone data can be used to enhance contact tracing and also to understand patterns of individual contacts. Whereas, at the population level, it can be seen that clustering or human mobility will help for the evaluation of the impact on NPIs with the quantifying changes to identify the required hotspots of some additional or different interventions that need to be applied.

Figure 1. Usage of mobile phone data of public health response and their possible biases [1].

Monitoring and evaluation of current interventions during the release:

The mobile phone data can be widely used in the telecom geolocation data to track the population movements for public health to date. Call Detail Records (CDRs) were been periodically collected by the mobile phone operators which contain the time stamp of preferred GPS location with a unique identifier for all subscribers. Hence, this readily available data offers high coverage to estimate the mobility patterns of individuals used with mobile devices. Also, it can be noted that a similar time-resolved GPS location data can be collected via for certain applications.

For the identification of potential hotspots and to capture the real-time population density, the NPIs effect can be monitored through subscriber density metrics with the combination of the recorded GPS location and timestamp of CDRs. The use of Bluetooth data (records of proximal interactions between Bluetooth-enabled devices) can be used to quantify for the real-time density or physical clustering of subscribers. Most of the contact tracing apps are collected via Bluetooth and/or GPS location data to create trails of contacts for a period (14-28 days). Rapid tracing is enabled with a very higher proportions of affected individuals. However, these apps will reduce the amount of time that a potentially infected person would have to infect others [1, 2].

Evaluation and the ability of mobile phone data for representing the population risk:

The primary advantage of using the mobile phone data is the possibility of collecting the bespoke data quickly in different areas to specific epidemiologic and social contexts urging this pandemic the situation, where the tailored responses are required for the usage of mobile phone data, fewer assumptions on the transportability with derived mobility and contact metrics across various populations are very essential.

Evaluating behavior for the determination of the appropriate resolution:

Mostly the mobile phone data is used to describe possible mechanistic drivers of transmission, and to understand aggregate for the population-level behaviors. On focusing the pandemic response shift from containment and mitigation to sustained surveillance, perhaps, even local elimination would be required for a very high targeted responses. But, there would be a promise aggregating individual-level data which provides the additional epidemiologically with the relevant information.

Mobile phone data integration for the decision making:

Integration of mobile phone data has showed greater promise for characterizing individual-level and population behaviors, although it remains considerable uncertainty around how to appropriately account for the patterns within these mobile data. Further, the policy decisions were been informed via the mobile data ‘which should be considered’ for ‘which populations’ and ‘which behaviors that would have been excluded from data'. Also for ‘which it approaches for the design, collection, analysis, or interpretation of mobile phone data.

Our SNB team have emphasize this research article to enrich our viewer’s knowledge about the utilization of mobile phone data analysis in this pandemic epidemiology. The main key feature is the near real-time nature of mobile phone data that should inform the response appropriately to rapidly-changing situations. Indeed, the effective utilization of mobile phone data will require direct, an iterative collaboration between researchers, mobile phone operators, and other public health officials. This form of reciprocal collaboration would have facilitate the rapid processing and bespoke data analysis which can help to ensure the participation of mobile phone operators continuously. At the same time, the interpretation of these data analysis is very essential to contextualize since they have been integrated from the public health decision making.

References

K.H. Grantzet al.,Nature Communications, 11, 4961(2020).
R.M. Anderson et al.,Lancet 395, 931 (2020).
D.Fisher, A.Wilder-Smith, Lancet 395, 1109 (2020).
C.O. Buckee, et al.,Science, 368, 145 (2020).

--- Dr. Y. Sasikumar

School of Materials Science and Engineering,

Tianjin University of Technology, China

Email:sasikumar@163.com

Website Profile: https://sites.google.com/view/drsasikumar

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