COVID lesson for better surveillance of serious diseases, Health News, ET HealthWorld


COVID lesson for better surveillance of serious diseasesby Prakash Kumar

People have different views on how COVID has been managed. However, it has taught us the use of new-age technology for the collection of data from the source, its processing and the use of processed information for near real-time administrative action. This knowledge can be used to track and forecast outbreaks of other serious diseases where laboratory tests are done to confirm the disease.

How is data being collected and processed for COVID? Accredited laboratories test people with symptoms. Along with the sample, the labs also collect the name, address and unique identification of patients. The test results are shared with the Indian Council of Medical Research (ICMR) at least once a day. If the patient is found to be positive, her/his address is used to identify the locality. Govt health functionaries use the contact details to provide help and advisory. In case the number of positive patients crosses the threshold limit, that locality is declared as a containment zone. The number of positive patients is also plotted on a GIS map to identify hot spots as a heat map (areas with high concentration shown as red and very low or nil as green and others in between). The visual representation on a map makes it easy for people to understand the impacted area. The data for a period like a week or a month shows how the disease has travelled/moved from place to place or is showing positive or negative growth at a place.

Integrated Disease Surveillance Programme (IDSP) under National Centre for Disease Control (NCDC) is tasked with surveillance of serious diseases. They collect data on these diseases and analyse them to detect outbreaks for instituting effective control measures in a timely manner. The method used for the collection and compilation of data is partly manual, leading to delays. Also, in many cases, data is collected weekly, which is a long period in certain circumstances for remedial action. As per the website of this program, “One of the main reasons for limited success in disease surveillance in the country has been and still is to some extent, the time consuming and labour intensive manual methods of data collection, communication, analysis and feedback for taking action.

Under IDSP program, NCDC has set up IT Systems in State Surveillance Units and District Units at 776 locations with facilities for data entry, training, video conferencing and outbreak discussion. This leaves the last point, i.e. healthcare units at the village, block or sub-district levels, where data gets compiled manually. This naturally leads to delays.

On the other hand, a good surveillance system must be comprehensive, enable near real-time data collection and analysis so that outbreaks can be found early for appropriate action like rushing medical teams with required medicines.

Can the method of data collection used for COVID be replicated for other serious diseases for near real-time data? That is the key. There are 12 diseases such as Dengue, Chikungunya, Japanese Encephalitis, Meningococcal Meningitis, Typhoid Fever, Diphtheria, Cholera, Shigella, Dysentery, Viral Hepatitis A, Viral Hepatitis E, Leptospirosis and Malaria reported under integrated disease surveillance program where laboratory confirmation is required. For these diseases, pathological labs could be mandated to share data on a daily basis with NCDC in an automated manner. If a lab can share COVID test data with ICMR, they can also do it with NCDC for these diseases.

There is a catch. Not all pathological labs are allowed to conduct COVID tests. Those who have been allowed to conduct COVID tests have upgraded their IT systems to automatically share data with the IT system of ICMR. However, there are thousands of labs, which are not conducting COVID tests but are conducting tests for serious diseases. How do they share the data on such tests with NCDC in an automated manner? With the increasing automation of lab tests and the use of IT systems, there will be very few who are still recording results manually on paper. Keeping in view the investment required for the IT system, sharing of data by these labs could be done in two phases. In phase-1, a mobile application could be used to upload gross data on tests conducted for these twelve diseases, which will contain the total number of cases under each disease and the number of positive cases. Knowing the location of labs, it will be possible for NCDC to generate a heat map for each disease at the sub-district or city/town level on a daily basis. This way, outbreak reports could be generated at the sub-district level daily.

Under phase-2, a computer-based application could be developed for these labs for sharing data at the patient level, as being done for COVID. Since it will have the PIN code of the patient’s address, a locality level map could be generated for each disease. This will help identify containment zone at the PIN code or locality level, which is more manageable in case of an outbreak. This will, of course, require a mandate from the government, the way it was done for COVID, to ensure that all labs start reporting. The data to be shared with NCDC should not include name, identity document number and mobile phone number as that may lead to a breach of privacy. Also, these are not required for disease surveillance.

Once implemented, this will make the States and the Central government get a better picture of an outbreak of serious diseases for a timely response as data will be coming on a daily basis. AI-based algorithms could be used to forecast outbreaks in near real-time for better response leading to saving of lives.

Prakash Kumar is CEO at Wadhwani Institute of Technology and Policy (WITP)





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