PDMV Suite

To date, responses to the Covid-19 pandemic have mainly comprised overly generalized assessments and remedies. Data tools and analysis techniques aimed at more locally specific situations and needs can improve on this. Wide-scale adoption of such localized response policies and measures could result in better outcomes: fewer deaths and illnesses, less social and economic disruption, more equitable conditions.

Responses based on excessively generalized data result in relatively poor outcomes in terms of health, economics, and politics. Control measures, health facilities provision, public health messaging, etc. could be improved by referring to more locally specific data on population health conditions and risks, and other relevant data, such as social and economic factors of susceptibility.  PDMV assists collection, mapping, viewing, and analysis of relevant data to provide insights needed for designing and implementing more informed, locally specific, and equitable pandemic/epidemic responses.

The 'problem' is actually a bundle of layered problems:

1. most visibly, inadequate pandemic responses leading to poor outcomes;
2. resulting from poorly designed, overly generalized response policies and measures, with often consequently insufficient public "buy-in";
3. engendered at least partly by insufficient attention to location- and situation-specific conditions, needs and resources.

The problem scale is enormous: essentially global, though varying in intensity. Case numbers and death counts again increase daily in many places with successive waves. Future outbreaks/resurgence and possible new variants are expected even in places showing improvement. Actual numbers of affected people are generally underestimated/-counted/-reported due to various limitations. (See reputable publicly available sources for up-to-date information, e.g. the Our World in Data website.) Economic impacts measure in the 100s of billions or trillions, and many societies' fabric is under severe stress due both to the disease itself and disruptions caused by disjointed, chaotic responses, making it more vulnerable to cascading effects of climate change, forced migration, political instability, and further health emergencies.

While PDMVSuite addresses the present Covid-19 pandemic, it is adaptable and applicable to other emergencies, providing better tools and knowledge to health workers and policy makers, and inspiring individuals and communities to protect themselves and each other.

Comprising primarily analysis/modelling and visualization capabilities aimed at exploring the impacts of various pandemic response options, PDMV Suite is targeted to non-expert users in health care, policy making, and community groups. Its capabilities include support for creating and comparing "what if ...?" scenarios (with an emphasis on risk identification rather than prediction, i.e. plausibility over probability) to help user groups' sensemaking efforts and promote their better understanding the possible impacts of response choices they make and/or of decisions imposed by others.

The fact that actual cases of infection and the severity of subsequent illness (if any) depend on many various factors -- including underlying health conditions, environmental and occupational risks, circumstances of exposure, etc. -- means, however, that typical analysis methods do not adequately account for differences in susceptibility. Compounded by the varying economic and social impacts across different communities, age groups, occupations, etc., broad-brush response policies and measures are therefore unlikely to succeed (excepting perhaps test/trace/isolate/support, where feasible.) Therefore, a core component of PDMV Suite is the capability to evaluate outcomes while accounting for relevant local differences.

For more about the relevant processes and technology, see "Core Tech" under the "More About ..." section below.

Targeting especially non-expert users in health care, policy making and community groups, the PDMV suite aims to help them better comprehend and respond to the current pandemic (and/or future outbreaks, other emergencies, etc.) by creating and comparing scenarios, and by designing and critiquing response policies and measures, with a particular emphasis on more localized assessments and responses suited to more specific situations, rather than broad-brush, "one size fits all" approaches. While not focused on underserved and disadvantaged communities, the accessibility of the PDMV tool suite could support such groups and their advocates in obtaining more equitable responses and outcomes.

The beneficiaries constitute potentially the entire population of the places where the PDMV approach is applied, inasmuch as the outcomes of Covid-19 itself and of the responses to it affect essentially everyone. More specifically, it potentially affects health workers dealing with the Covid-19 pandemic/epidemic (and/or future health emergencies) and also the patients in their care, as well as the people making policies to respond to the pandemic/epidemic, and of course all who are subjected to the effects of those policies.

Still more specifically, it is aimed to be used by health researchers and practitioners advising response policy makers (at various levels, both public and private), preferably in collaboration with those policy makers. Community groups, advocates and others concerned with influencing policy would also potentially benefit. Ongoing development of the solution needs to engage primarily these three stakeholder groups (health, policy, community) for tailoring its capabilities to address their needs for more informed work with pandemic/epidemic -related data and response options.

PDMV Suite works by supporting collection, mapping, viewing, analysis, scenario planning and sensemaking of and with more locally specific relevant data. This means that future responses to CoVid-19 and/or other outbreaks could be tailored to give better outcomes in terms of health, economics, and politics, based on accounting for more specific needs and resources of more specific populations and circumstances. Thus, responses such as test/trace/isolate/support, surge testing, surge vaccination, localized "lockdowns", curfews, PPE provision, health facility expansion, emergency health personnel mobilization, training, public messaging, and so forth can be compared, selected, and refined as appropriate to the particular social, economic, political, and material/physical conditions and needs present among the local populace or other affected groups.

Ultimately the impacts of PDMV use would be: first, to help professionals, policy makers and others occupationally concerned with pandemics/epidemics to do their jobs better; second, to help advocates and groups adversely affected by such events to better understand what is happening and how to respond; and third, but not least, to help potential and actual victims suffer as little harm as possible.

The proposed data tool suite aligns with more than one dimension above, specifically: it addresses the need for tools and knowledge among health workers (and policy makers) to support effective responses -- but without aiming at outbreak detection; furthermore, it addresses individuals' and communities' needs for inspiration/nudging and knowledge.

Addressing how responses to disease outbreaks can be improved, it enables and promotes design of more nuanced response policies and measures -- and potentially also improved public acceptance of such --  which are more appropriate to the specific localities where they are implemented, rather than being excessively generalized, broad-brush responses.

"Prototype" is the development stage category chosen because the data tool suite described has been undergoing building/testing prior to piloting, roll-out, etc.

However, "pilot" might have been chosen, because PDMV Suite has undergone extensive development -- with much added functionality since the previous version(L?) -- and internal testing, so that preparations have begun for designing and carrying out pilot studies, including seeking partners to help in organizing these. Thus, it is basically "pilot-ready". Solve contacts/resources would be sought especially to help support advancing this stage.

Competing projects typically fall into the category of "dashboards", which have proliferated during the course of the pandemic so far. However, these tend to aggregate data at too large scales (e.g. countries, states/provinces, entire cities) to give really meaningful insights regarding the progress of infections and how to design appropriate responses. Also, such projects merely record basic counts of tests, cases, illnesses, deaths, vaccinations, etc. -- sometimes providing minimal context such as expressing these on a normalized basis (e.g. counts-per-million population), or breakdowns by age groups -- but other important contextual data are missing. Most importantly, such dashboards don't support comparison of scenarios under different response options, for example.

Specialist epidemiological modelling tools, on the other hand, while capable of scenario comparisons and projections, also tend to make overly broad generalizations regarding populations' susceptibility to contracting Covid-19, which can result in mis-estimation of outbreaks' extent, location, and severity, with corresponding mis-design and -implementation of responses (control, care, messaging, etc.) In any case, they are typically inaccessible to non-experts.

The PDMV approach innovates by combining the best features of existing pandemic/epidemic data tools and augmenting them with support for 'sensemaking' to help non-experts reason and communicate effectively about their discoveries. Whether this would have any catalytic effects beyond the intended benefits remains to be seen.

Currently none, as it is still in prototype stage, preparing for piloting.

Consequently, estimates of future service levels are necessarily very approximate, but something on the order of 10-100 K within one year after pilot studies, and 10-100 M within 5 years are conceivable (including affected non-users.)

As related above, measurement of progress toward impact goals is not yet applicable to PDMV Suite at this point, as pilot studies remain to be carried out. (Though arranging those studies will constitute some measure of progress in itself.)

It is anticipated that some market-oriented metrics of impact -- such as number of users, referrals, trials, publicity, revenues, etc. -- will eventually come into play, as well as indicators on pandemic-related trends in health, economics, community engagement, and the like -- although these latter would be exceedingly difficult to attribute to one or another particular intervention.

The PDMV Suite is one project within O-Design Consulting & Research, carrying forward knowledge from its precursor project, PanDa-V-M (see MIT Solve Health ___ Challenge 2020). When ready for roll-out, PDMV would be handled by a separate entity established for the purpose.

varies as needed

This application is motivated by the wish to continue development and dissemination of the PDMV approach with support from a community such as MIT Solve, particularly with respect to: receiving advice on key points of the pilot-grow-scale process; navigating the not-for-profit sector; and  networking with potential partners, consultants and/or co-workers.

Also important in the near term are the design, organization, and completion of pilot studies for the solution, a key area in which help would be sought within the Solve community.

The key pilot studies stage will require hiring of experienced persons to design and/or carry them out. Public relations inputs (along with networking) would foreseeably help with gaining support for the pilot studies, as well as for improving visibility and access in subsequent stages. Pilot study design and the digestion of results will entail inputs in the areas of monitoring and evaluation. Technology related inputs would contribute to extending and/or adapting the PDMV Suite's functionality and improving its performance.

In order to address the input needs described, and also to expand the reach and impacts of pilot studies and subsequent stages, partnerships are sought with foundations and other NGO's -- including advocacy groups, thinktanks, and civil society organizations -- as well as private-sector actors in public relations and technology, in addition to relevant networking and possible collaborations within the more immediate Solve community.

The United States of America's disjointed pandemic response during most of the Covid-19 pandemic to date have made it, unfortunately, a prime proving ground for the proposition that more locally appropriate response policies and measures would produce better outcomes. For despite the variety of responses made at various levels of administrative authority and other leverage points -- with thereby some potential for situation-specificity -- coherent reliance on rational assessments of needs, available resources, and feasible measures has been little in evidence. At the same time, the USA also has some positive characteristics which make it a potentially fertile ground for the PDMV approach, such as still-strong traditions of self-reliance and community action, as well as an absence of many features of more authoritarian societies. Receipt of this prize would support PDMV's development and dissemination primarily through USA-based pilot studies as well as feedback solicitation regarding health workers', policy makers', and communities' USA-specific needs and wishes vis a vis health emergency response option study, critique and formulation, with a particular emphasis foreseen on public messaging and its role in promoting adoption of equitable and effective emergency responses.

These prize-supported inputs would all contribute to strengthening PDMV Suite's capabilities with respect to promoting the design and implementation of better pandemic/epidemic response policies and measures.

As official, broadly generalized pandemic/epidemic policies and measures rarely if ever address refugees specifically -- except largely by explicitly excluding them from protections and benefits available to others -- the PDMV Suite's approach to promoting localized, situation-specific responses suits it well to also serving refugees' needs in respect to helping them better understand response options and formulate for adoption responses feasible within and suited to their own conditions. It can also help refugees' advocates and health workers in similarly studying, critiquing, and formulating appropriate responses 

This prize would further the PDMV Suite's accommodation of refugees' needs by steps such as: outreach to refugee groups for pilot studies and other feedback; publicization of refugees' issues vis a vis tailoring of pandemic/epidemic responses to their particular conditions; and consultations with refugee advocates and other groups/organizations working on refugee issues under pandemic conditions.

These would all contribute to strengthening PDMV Suite's capabilities with respect to support for formulating, critiquing, and implementing more equitable and effective pandemic/epidemic response policies and measures.

Considering that official pandemic/epidemic policies and measures rarely if ever address women specifically, the PDMV Suite's approach to promoting localized, situation-specific responses suits it well to also serving women's needs in this respect.

This prize would help enable emphasizing this aspect of PDMV Suite, via for example:

• organizing pilot studies specifically catering to women and women's issues;
• hiring of women for development, publicization, distribution and other key functions in the project's further progress;
• incorporating functionality which would serve specifically women's needs.

These would all contribute to strengthening PDMV Suite's capabilities with respect to support for formulating, critiquing, and implementing more equitable and effective pandemic/epidemic response policies and measures.

The PDMV Suite's data analysis and modelling capabilities consist partly of AI/ML and other Data Science aspects which support predictive and interpretive actions relating to, for example, similarities in underlying conditions of different local communities and other affected groups, which may presage similar outcomes from similar pandemic/epidemic responses.

Receipt of this prize would support strengthening these components by helping in: obtaining further expert inputs; devoting additional development effort to enhancing these aspects; and enabling additional feedback solicitation regarding user-preferred capabilities and implementation strategies for AI within the pandemic/epidemic context.

These would all contribute to strengthening PDMV Suite's capabilities with respect to support for formulating, critiquing, and implementing more equitable and effective pandemic/epidemic response policies and measures.