Question

Imagine that you are a public health nurse, and you and your colleagues have determined that the threat of a deadly new strain of influenza indicates a need for a mass inoculation program in your community. What public health data would have been used to determine the need for such a program? Where would you locate public health data? What data will be collected to determine the success of such a program? How might you communicate this to other communities or internationally?

Answer 1

1. Digital technologies are being harnessed to support the public-health response to COVID-19 worldwide, including population surveillance, case identification, contact tracing and evaluation of interventions on the basis of mobility data and communication with the public. These rapid responses leverage billions of mobile phones, large online datasets, connected devices, relatively low-cost computing resources and advances in machine learning and natural language processing. This Review aims to capture the breadth of digital innovations for the public-health response to COVID-19 worldwide and their limitations, and barriers to their implementation, including legal, ethical and privacy barriers, as well as organizational and workforce barriers. The future of public health is likely to become increasingly digital, and we review the need for the alignment of international strategies for the regulation, evaluation and use of digital technologies to strengthen pandemic management, and future preparedness for COVID-19 and other infectious diseases.
2. National-level surveillance data are compiled from case notification reports of national notifiable diseases and conditions submitted from the state, territory, and selected local health departments to CDC.Data can be used to evaluate program impact, to determine appropriate public health interventions, to monitor progress, to determine populations to target for an intervention, to determine barriers to care, and to influence public policy.
3. EGH’s goal is to help inform current and future policies and programs in global health based on what has worked in the past. We do this by rigorously analyzing the success of countries that have made extraordinary progress. In the midst of a constantly evolving pandemic like COVID-19, it is not a simple matter to identify countries that have been most effective and therefore have the most to teach the rest of the world about best practices. Based on the current data, we developed a methodology to help us identify these emerging success countries. There are considerable limitations to the selection methodology due to the evolving nature of the pandemic including incomplete data, evolving case definitions, and the fact that the ultimate outcomes are unknown. As you will see, it is not possible to identify emerging success stories, or exemplars, using just one indicator. Each data point has nuanced drivers and meanings, making it important to triangulate and look across multiple indicators to identify countries that have had success to date in managing the pandemic. The daily rates of confirmed deaths follow very different trajectories in countries. The steeper the slope of the curve, the faster the rate of increase in deaths. Countries at the top of the first graph below have had the most deaths, but some of those countries also have larger populations. The second graph below shows deaths per capita to account for differences in population. These graphs suggest that some responses have been much more effective at reducing deaths from COVID-19 than others, but they do not tell us why they are more effective.
Detect, Contain, Treat :-
To help shed light on why some responses are more effective, we initially started with a four-part framework for epidemic preparedness and response: prevention, detection, containment, and treatment. Because transmission of COVID-19 is ongoing, it is too soon to determine if any country will ultimately succeed at prevention, so we excluded the prevention phase from our analysis. We selected multiple indicators for the detection, containment, and treatment phases that could help us identify which countries are excelling at any given phase. This enables us to identify countries that show emerging success at each phase, which makes it more likely to glean detailed insights that will be useful for other countries.On this page, we walk through a total of nine graphs addressing all three phases, describing what each graph can and cannot tell us about a country’s response. These graphs are updated daily, so the countries that stand out now might change over time. Some countries with early positive outcomes were unable to sustain their success, whereas the situations in other countries gradually improved. To see the results on any given day of the pandemic or to reflect any range of dates, adjust the slider bar below the graphs. This will turn single data points into line graphs.
Country Selection Criteria
To ensure that our process is rigorous and that other countries can use lessons from our exemplars, we applied the following three criteria to select countries for our analysis:

We included only countries with populations greater than 5 million. This minimizes the likelihood that apparent trends are background noise in the data, and to ensure we can extract lessons applicable to the many large countries that are currently struggling to contain the pandemic.
We included only countries in which at least 21 days had passed since the 100th confirmed case.
We wanted to include only countries whose response had been going on long enough and with sufficient numbers of cases to be properly assessed, recognizing that in some countries, low prevalence may be the result of effective containment.
We included only countries with high-quality data on testing, cases, and deaths.
Many countries have been slow to publish testing data, preventing proper analysis at this time. Our testing database has been developed with a clearly documented checklist of criteria for inclusion; definitions and units; and detailed descriptions for each country so users can understand the comparability of data from different countries.
The number of countries meeting the second and third criteria will grow over time, increasing the number of countries that can be included in the analysis.