PanDa-V-M Data Tools

Current pandemic/epidemic responses are typically based on excessively generalized data, resulting in relatively poor outcomes in terms of health, economics, and politics. Responses (e.g. control measures, health facilities provision, public health messaging) could be improved by referring to more locally specific data on population health conditions and risks, as well as other relevant data. The PanDa tools aim to enable collection, mapping, viewing, and analysis of relevant data so as to provide insights needed for designing and implementing more informed, locally specific, and equitable pandemic/epidemic responses.

The proposed solution is motivated by and addresses primarily the present CoViD-19 pandemic, but it is also potentially applicable to other concurrent health/disease emergencies, as well as to future outbreaks of CoViD and/or other pandemics/epidemics. The specific problem addressed concerns the present apparent inadequacy of pandemic/epidemic response design due to insufficiently specific (i.e. spatially localised and demographically detailed) data collection and analysis. 

The scale of the problem is enormous (essentially global but heterogeneous in intensity). CoViD case numbers and death counts still increase daily in many places, future outbreaks/resurgence are expected even in places currently showing improvement, and 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.)

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, __ , this makes broad-brush response policies and measures unlikely to succeed. 

The PanDa-V-M tools support design of better informed, more nuanced pandemic/epidemic responses by enabling collection, viewing, analysis and modelling of pandemic/epidemic data and data on relevant other factors (e.g. underlying health conditions, environmental and occupational hazards, demographic data, etc.) ...

The target for this work is potentially the entire population of the places where it is applied, inasmuch as the outcomes of the CoViD-19 disease 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 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.

The solution aligns with the Health Security and Pandemics Challenge primarily by addressing how responses to disease outbreaks can be improved. Specifically, it aims to improve the design of more nuanced response policies and measures, which are more appropriate to the specific localities where they are implemented (rather than being excessively generalized, broad-brush responses). This means that future responses to CoVid-19 and/or other outbreaks can be tailored to give better outcomes in terms of health, economics, and politics, based on more specific needs of more specific populations and circumstances.

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, counties, cities) to give really useful insights regarding the progress of infections and how to design responses to these.

Specialist epidemiological modelling tools similarly tend to make overly broad generalisations 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.)

The PanDa solution innovates by combining the best features of existing pandemic/epidemic data tools and augmenting them.

Full evidence of efficacy will depend on pilot and other trials, with response policies and measures designed using input from PanDa-V-M tools (followed by implementation of those responses, and evaluation of outcomes).

Preliminary study indicates that use of the tools would support more detailed/nuanced design of control measures (such as movement/contact restrictions), provision of additional healthcare facilities/capacity, and (re-)allocation of existing treatment capacity on appropriately localised population data.

Parent is a Private Limited company registered in SG; structure for PanDa-V-M remains to be determined.

Varies as needed

PanDa-V-M as a separate entity could be run as a not-for-profit (or perhaps alternatively a B-Corp), spun off from the Visual Analytics aspect of the core Computational Design Intelligence consultancy (Odessys Pte Ltd).

Initially reliant on donations, grants, and "angel" investors, with eventually some possible income from nominal (or donation-/grant-subsidised) charges for tools and/or services.