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Strategic Risk Assessment model for COVID-19 infections in India With Special analytical representation for Maharashtra State

Vishakhadutt Patil

Correspondence Address :

Healthcare Technology Solutions Architec
SAN Innovative Systems Pvt.Ltd
Mumbai

Received on: June 22, 2020, Accepted on: July 07, 2020, Published on: July 14 2020

Citation: Vishakhadutt Patil (2020). Strategic Risk Assessment model for COVID-19 infections in India With Special analytical representation for Maharashtra State

Copyright: 2020 Vishakhadutt Patil. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract
Creating a risk assessment model at the district level for tracking COVID-19 infections in India is important for identifying populations at risk. Risk assessment can additionally be computed through case studies, cohort studies and cross sectional studies. 
The analysis of geographically logical segregation of population (district wise) can qualify as a valid cohort study and can be compared with actual incidence and prevalence of cases. This analysis should consider known determinants and risk factors such as high volume of international and domestic visitors, high rates and intensity of migration, and high population density per se.
The paper draws up a strategic framework based on these factors and assesses the risks based on its implementation. It collates information about incidences of COVID 19 infection in all 736 districts of India and specifies its findings in 36 districts of Maharashtra State of India and finds that Current Actual Scores (as on April 11, 2020) for the state can be used to assess risk of COVID 19 incidence in the state.
Keywords: COVID 19 India risk assessment, Incidence, Prevalence, Mortality, Odds ratio, Morbidity, Relative risk, Hazards ratio, Standardized mortality ratio, Standardized incidence ratio, COVID-19 Risk, District Level COVID 19 Risk, Risk assessment model, District level, COVID infections India, Identifying populations, Cohort sectional analysis, Geographically logical segregation, Risk factors, High volume international domestic visitor rates , Risk Assessment Tools, Morbidity specific categories, SIR, SMR
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Background
Risk Assessment Tools to predict morbidity and mortality in specific categories of patients are now being used in various hospitals and health care institutions [1]. However they rely on symptomatic and clinical assessment of persons who are already
reporting sick. They have not been readily or extensively used to assess surveillance and screening strategies[2]. as they are prepared using clinical experience rather than deterministic or probabilistic modeling that is based on statistical analysis and assessment of fixed or random variables along with standard deviation.

Objective
The objective of this paper is to present a strategic model to assess the risk of a population classified according to risk groups residing in a given geographical location. The assessment is not based on behavior and social and familial transactions that are
identifiable with community based interactions.
The purpose of this model is to propose a strategy aimed at restricting complete isolation measures such as lockdown or quarantine to limited geographical regions or demographic profiles that could be termed high risk based on their risk assessment only.
Risk assessment can only be confirmed based on epidemiological studies of cases, cohorts and cross sectional analysis. Important effect measures such as the relative risk (RR), hazard ratio (HR), standardized incidence ratio (SIR), standardized mortality ratio (SMR), and odds ratio (OR) can also be calculated[3].
The purpose of this model is to propose a strategy aimed at restricting complete isolation measures such as lockdown or quarantine to limited geographical regions or  demographic profiles that could be termed high risk based on their risk assessment only.

Materials and methods
All districts of India are listed according to population, area and density. The following considerations were taken into account.
1. Population Density more than 1000 persons per square kilometer [5] were assigned a score of one.
2. Districts containing an international airport [5] were assigned a score of one.
3. Districts with a large number of foreign visitors[6] were assigned a score of one. Therefore districts with an international airport would automatically be assigned a score of one. Also districts that were known to neighbor the international airports and contained a reliable and verified account of high foreign visitor traffic such as Raigad and Thane Districts adjoining Mumbai and Mumbai Suburban districts would also be assigned a score of one. Similarly districts having popular tourist destinations known to attract higher foreign visitor traffic are also assigned a score of one (Such as Agra, Udaipur, Munnar, and so on). Furthermore places of religious significance known to be pilgrimage destinations for the faithful from all over the world such as Amritsar, Ajmer, Ujjain, and so on – were also assigned a score of one.
4. Districts with a large number of domestic visitors [7] were assigned a score of one. These automatically included state capitals, districts with pilgrimage sites[8] such as Nashik, Tirupati, Mathura and so on and also headquarters of railway zones such as Gorakhpur.
5. Border districts (Districts with international borders): Sixteen states and two union territories share international borders with neighboring countries[9]. Border districts of these states have been listed for risk assessment purposes. The reason for including border districts is the presence of Integrated Check Posts (ICP) [10] , Land Customs Stations (LCS) [11], Border Bazaars or Hats[12], and designated land border crossing zones in many of these districts. Depending on the extent of people to people contact as well as the work done by Border Area Development Program (BADP) [13] – a strategic call can be taken on whether to include these districts or not. Each District is given a score of one.
6. Districts with Major Outward Migration: India does not have a single definition to identify migrants. The two major agencies (The Census of India and National Sample Survey Organization (NSSO)) which collect information on migration use different criteria, though both define migrants based on the change in place of residence. According to the Census of India, an individual is classified as a migrant if [s]he has changed his place of residence in the past from one village/town to another village/town. The Census also uses a place of birth classification. Change in place of residence within a village/ town or temporary change in the place of residence due to religious visit, official tour, sightseeing, medical treatment etc. are not considered as migration. On the other hand, NSSO defines migration on the basis of last usual place of residence, which (unlike the Census) is defined as a place where one has stayed continuously for a period of six months or more. If the present place of residence of an individual differs from the last usual place of residence, then [s]he is classified as a migrants. According to the Census, 30% of the total population in 2001 were migrants. Based on the NSSO definition, 28% of the total population in 2007-08 were classified as migrants[14]. Taking all sources into consideration for key male inter-state out migration districts from rural to urban areas, it can be observed that only 17 districts account for a quarter of all male out-migration across state boundaries. Following this, another 37 districts account for an additional 25%, that is 54 districts account for half the male inter-state out-migration in the country. These districts are concentrated in Eastern Uttar Pradesh and Bihar, with certain districts also placed in other states, such as West Bengal, Odisha, Karnataka, Maharashtra, Rajasthan, and districts in Western Uttar Pradesh.

Each district that falls in this category is given a score of one.

7. Districts with high inter-state out-migration intensity: Migration intensity is a generic term that encompasses both migration rate and migration probability[15]. Taking distinct measurements of migration intensity[16], districts with a high level of inter state out-migration intensity (inter-state male out-migrants from rural areas) have been determined [17] . These districts have been assigned a score of one.
8. Migration Risk Assessment Score: Points 6 and 7 represent a migration risk assessment score. Though the study has reasonably accounted for intrastate migration given that state capitals, headquarters of railway zones, tourist and pilgrimage centres, and areas with high population density have been factored into the quantitative scoring system. However that still leaves out interstate migration. There have been reasonable assessments of rural to urban interstate migrations at the interstate level. Given that there are districts that account for a large number of interstate rural to urban and urban to urban migrations, we have assigned a Migration risk assessment score for outward migration and migration intensive districts. The assessment score would be 1 for outward migration and 1 for migration intensive districts.
9. Other Factors to be considered may be districts with hill stations in India that may get a large number of visitors; Districts with Ports and ferry services, or districts that have the busiest bus and railway stations. This needs consolidation of data at a more detailed level. Therefore only a maximum score of 7 is currently considered.

Scores and Strategic Recommendations

Quantitative Recommendations

1. Districts with a maximum score of 7 may represent a risk of community spread of over 90% to 95%and may not only need to be completely locked down but also need to be extensively submitted to screening and surveillance measures.
a. All movement to and from the districts must be individually and specifically monitored.
b. Transport teams that carry essential supplies and people to and from these areas need to be requested to be put  under surveillance and asked to report any untoward symptoms.
c. Additionally high risk groups must also be requested to submit to surveillance and asked to report any untoward symptoms.
d. Areas must be sanitized and areas where patients are detected must be cordoned off or declared containment areas and everyone who has come into contact with the patients must be screened.
e. The lockdown period must be at least 45 days from the detection of the last case.

2. Districts with a maximum score of 6 may represent a risk of community spread of 90% and may not only need to be completely locked down but also need to be extensively submitted to screening and surveillance measures.
a. All movement to and from the districts must be individually and specifically monitored.
b. Transport teams that carry essential supplies and people to and from these areas need to be requested to submit to surveillance and asked to report any untoward symptoms.
c. Additionally high risk groups must also be requested to submit to surveillance and asked to report any untoward
symptoms.
d. Areas must be sanitized and areas where patients are detected must be cordoned off or declared containment areas and everyone who has come into contact with the patients must be screened.
e. The lockdown period must be at least 36 days from the detection of the last case.

3. Districts with a maximum score of 5 may represent a risk of community spread of 80% and may not only need to be completely locked down but also need to be extensively submitted to screening and surveillance measures.
a. All movement to and from the districts must be individually and specifically monitored.
b. Transport teams that carry essential supplies and people to and from these areas need to be requested to submit to surveillance and asked to report any untoward symptoms.
c. Additionally high risk groups must also be requested to submit to surveillance and asked to report any untoward symptoms.
d. Areas must be sanitized and areas where patients are detected must be cordoned off or declared containment areas and everyone who has come into contact with the patients must be screened.
e. The lockdown period must be at least 27 days from the detection of the last case.
4. Districts with a maximum score of 4 may represent a risk  of community spread of 70% and may need stringent movement
restrictions along with extensive screening and surveillance for community based transactions.
5. Districts with a maximum score of 3 may represent a  risk of community spread of 60% and may require screening for contact mapping and surveillance
6. Districts with a score of 2 may represent a risk of community spread of 50% and may require surveillance and screening/ evaluation of high risk groups
7. Districts with a score of 1 may represent a risk of community spread of 40% to 50% and may require screening / evaluation of high risk groups
8. Districts with zero score should be first evaluated against qualitative measures (such as number of actual cases detected, presence of a floating population or presence of significant number of non-domiciled persons/migrants who have come there for employment) before being temporarily excluded from the lockdown and quarantine measures.

Qualitative Recommendations
Actual Cases Detected scoring system The present scoring system must be combined with a district level mapping of
actual cases detected as indicated in[18] https://www.rediff. com/news/report/rediff-labs-mapped-district-wise-covid 19positive-cases-in-india/20200329.htm and
a. A score of 4 must be assigned to districts where the number of cases is more than 50.
b.  A score of 3 must be assigned to districts where the number is 11 to 50
c. A score of 2 must be assigned to districts where the score is 1 to 10
d. A score of 1 must be assigned to districts where it is known that Covid-19 positive persons may have visited or travelled. All districts have been marked as having had a visitor who may have been a carrier for COVID-19.
Therefore all districts have been assigned a minimum score of 1.
1. Combined Quantitative and Qualitative Score Given that the combined qualitative score can be at a maximum of 12, the
following can be the strategies employed:
a. Combined Score 12 –
i. Surveillance of all workplaces, shops and establishments. Every registered establishment asked to submit details of people at the workplace including owners and directors and details of untoward symptoms. Maintain a register of all domiciled individuals according to voting cards issued, school admission records and operational bank accounts and mobile phones operational in the area. Track each individual telephonically and ask them through a recorded call about their details as well as details of their family members.
ii. Have a 24x7 helpline to attend to callers who call in with suspected symptoms
iii. Track each patient who reports with fever and cough and request them to be tested.
i v.  Prepare for admitting at least 50% of the population of that area.
b. Combined score 11 –
i. Surveillance of all workplaces related to Primary occupations such as agriculture, hunting. forestry, fishing, mining and quarrying (as detailed in National Industrial Classification codes of 2004 (NIC) 01-14; Manufacturing units, restaurants, catering services (as detailed in NIC 15-37); Public Services such as listed in NIC 40-41 plus Transport via railways (NIC 6010), National Postal activities (NIC 64110), and Public Administration (NIC 751, 752 and 753), Construction activities as listed in NIC 45; Traditional services including wholesale and retail trade, hotels and restaurants, transport, storage and communications (NIC 50-52, 55, 60-64, except 6010 and 64110; and Modern services that include Financial Intermediation, Real estate, renting and business, education, health, social work, other community, social and personal services (NIC 65-74, 80, 85, 90-99, excluding 751, 752, 753). These are places where people must be surveyed for fever, cough and respiratory symptoms  suggestive of COVID-19 infection.
ii. Screen neighboring districts.
iii. Surveillance of all high risk groups.
i v.  Extensive contact mapping,
v.  Isolate all contacts - quarantine all close contacts and family of positive cases.
vi. Make arrangements for hospitalization of up to 20% of screened people and ICU facilities for up to 15% of the
number of active cases
c. Combined score of 10 –
i. Surveillance of all workplaces related to Primary Occupations, Manufacturing units, and construction activities
ii. Screen Neighboring Districts
iii. Surveillance of all high risk groups.
iv.  Extensive contact mapping,
v.  Isolate all contacts - quarantine all close contacts and family of positive cases.
vi. Make arrangements for hospitalization of up to 15% of screened people and ICU facilities for up to 10% of the number of active cases
d. Combined Qualitative Score 9 – Screen neighboring districts. Surveillance of all high risk groups. Extensive contact mapping, Isolate all contacts quarantine all close contacts and family of positive cases. Arrange for ventilators for up to 10% of the number of active cases
e. Combined Qualitative Score 8 –Extensive contact mapping, Surveillance of all high risk groups. Isolate all contacts  quarantine all close contacts and family of positive cases.  Arrange for ventilators for up to 7%% of the number of active cases
f. Combined Qualitative Score 7 –Extensive contact mapping, Quarantine all close contacts and family of  positive cases. Arrange for ventilators for up to 5% of the number of active cases
g. Combined Qualitative Score 6 –Extensive contact mapping, Quarantine family of positive cases. Arrange for ventilators for up to 4.5% of the number of active cases
h. Combined Qualitative Score 5 –Extensive contact mapping, Isolate positive cases. Arrange for ventilators for up to 4% of the number of active cases
i. Combined Qualitative Score 4 –Extensive contact mapping, Isolate positive cases. Arrange for ventilators for up to 3%
of the number of active cases
j. Combined Qualitative Score 3 – Extensive contact mapping, Isolate positive cases. Arrange for ventilators for up to 2% of the number of active cases
k. Combined Qualitative Score 2 – Extensive contact mapping, Isolate positive cases. Arrange for ventilators  for up to 1% of the number of active cases
l. Combined Qualitative Score 1 – Extensive contact mapping, Isolate positive cases.
2. Targeting Hospital Beds and other health facilities and sanitization measures based on combined qualitative and quantitative score would be an effective strategic way of distributing resources and focusing attention.
3. Other Recommendations: Active Cases scoring system here the percentage of the number of cases determines the scoring.
Therefore
a. A score of 5 must be assigned to districts where the percentage of active cases is greater than 90% of the total number of detected cases
b. A score of 4 must be assigned to districts where the percentage of active cases ranges from 75% to 90% of the total number of detected cases
c. A score of 3 must be assigned to districts where the percentage of active cases ranges from 50% to 75% of the total number of detected cases
d. A score of 2 must be assigned to districts where the percentage of active cases ranges from 25% to 50% of the
total number of detected cases
e. A score of 1 must be assigned to districts where the percentage of active cases ranges from 1% to 25% of the
total number of detected cases Given that restriction on movement and social interaction is the only agreeable strategy for preventing the spread of COVID 19 infections[19], it is imperative that these recommendations be seen as a strategic exit plan for easing of restrictions.
Findings and Results
Quantitative Score Analysis – All India
Out of the 736 districts of India, no district reached a maximum score of 7.
Out of the 736 districts of India, no district reached a maximum score of 6.
Only 3 districts reached a maximum score of 5. Our findings are given in Table1 below
Therefore 431 districts that have scored zero do not have any major quantitative risk at all under this proposed model. Let us assess these districts via qualitative scoring that depends on actual cases reported (Table 1).
A. Current Actual Scores (as on April 11, 2020) and combined score with Quantitative score
As an example, we have taken Maharashtra (studies for other states are also conducted likewise) to demonstrate our findings as shown in (Table 2).
By this analysis none of the districts reach a maximum score of 12 and the highest combined score that a district has reached is 8 and that is Mumbai City. Pune District comes next with a maximum combined score of 7. There are two districts that have a combined score of 6 each; there are 8 districts that have a combined score of 4; there are 13 districts that have a combined  score of 3; and there are 11 districts that have a combined score  of 2 as all districts have been marked as having had a visitor who may have been a carrier for COVID-19.

Table 1: State wise Quantitative Score. The figure is created by the author and not copied.


Table 2: Combined Current Active Score and Quantitative Score for Maharashtra. The figure is created by the author and not copied


Discussion
It is clear that areas, which are at a risk for community based spread or uncontrolled spread of COVID-19 infection should be  isolated and restrictions on movement within these areas as well  as to and from these areas must be in place. However it is equally important that resources must be allocated for testing effectively and for managing outbreaks and preventing deaths. These resources can be allocated to areas that can be identified through a model of quantitative appraisal and continuous qualitative assessment.  A rational model based on these factors can also help ease restrictions on areas that are clearly at minimal risk at least from the standpoint of allocating resources to areas that need it the most.

Interpretation
The framework discussed above can be a starting point for further improvement in risk assessment as well as a viable model for long term control and prevention measures. It can be the basis for Cohort studies (for estimation of incidence rate and mortality rate as descriptive measures of frequency, as well as relative risk (RR) or hazard ratio (HR) as comparative effect measures. Standardized incidence ratios (SIR) or standardized mortality ratios (SMR) are used for comparison with the general population.), Case-control studies, (for retrospective assessment  of exposure and odds ratio (OR)) and further Cross-sectional  studies.

Limitations and Falsification
1. Limitation of risk identification: The analysis and identification of risk factors are based on the currently known and documented features, transmission mechanisms and epidemiology of COVID-19 infections[14]. Any new mode of transmission or epidemiological determinant will grossly change the number as well as the relevance of the risk factors identified.
2. Limitation of predictability: The assessment of relative risk (RR), hazard ratio (HR), standardized incidence ratio (SIR), standardized mortality ratio (SMR), and odds ratio (OR) will continuously change[20] based on the incidence and prevalence of the disease in a given district. This conceptual model does not have any predictive value. It is only an assessment framework for strategic decision making based on the current set of data available at any given point in time.
3. Limitation of Viability: The model is based on the data sources available to the author. Any different data source that contradicts the available data (Such as number of districts or figures of migration intensity) will change the assessment results provided by this model. A suitable algorithm for machine learning based on this model will be able to correct itself as well as incorporate new factors and determinants based on the new sources of data made available to it.
Generalization: This model could serve as an assessment tool in risk evaluation in other regions of South Asia as disease determinants and risk factors may be similar to India and other models specific to UK[21] and other European countries[22] or the American Continent[23] may not be culturally tenable. The model suggested in this paper may be used as an additional  benchmarking reference in conjunction with administrative  strategic planning and should not be used as a direct reference or replacement to official strategy. There should be no wrong notion in the reader’s mind that this replaces official policy, announcements or guidelines. This is an entirely new model proposed by the author and not based on any previous models but on observations and analysis of disease determinants specific to the community agnostic social and cultural behavior found and measured in India. Social impact of COVID 19 Infection spread in India needs to be assessed independently based on other studies[24-34] conducted in the recent past.
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Tables & Figures

Table 1


Table 2

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