Method to mitigate allergen symptoms in a personalized and hyperlocal manner

1 . A system directed to monitor the impact that environmental conditions may have on a user, the system comprising:

a plurality of base stations in communication with a network comprising a remote server in communication with the network;

a plurality of indoor and outdoor sensors in communication with the base stations, the sensors configured to monitor environmental conditions in proximity to the user; and

a communication device including an app in communication with the network;

wherein the remote server includes a computerized processing device and is operative to:

access stored information indicating one or more user information, wherein the user information is selected from the group consisting of age, location, daily activity tracking of allergy symptoms, medications, quality of life (QOL), date, day of week, season, geography, pollution, pollen, weather, medication, treatment regime (including medication response, or no response, data), and indoor air quality;

receive information from the sensors via the base stations indicative of one or more measures of environmental conditions, receive information from the communication device indicative of the user information measuring an outcome of the user's information, the measuring comprising:

calculating, for the user, on the computerized processing device, the change in symptom and allergen outcome relationship over time (SnPn/dt) (or the modeling of nested/clustered data, wherein there are multiple individuals in each cluster) using a multivariate regression or probabilistic approach;

generating and including on the computerized processing device an analysis engine for analyzing potential treatment options based on the user's information regarding the user's response (or no response) to medication in combination with the calculated change in symptom and allergen outcome relationship over time (SnPn/dt) data by i) using for each SnPn, Y (Sn×dt)=X (Pn×dt) βn (αi×dt)+ϵ, where Y equals dependent variables, X equals predictor variables, S equals specific symptoms, P equals specific allergens, n equals number of variables, β equals allergen coefficients, αi equals time dependent coefficients dt equals time change, and ϵ equals intercept and ii) applying at least a portion of the crowdsourced information acquired from the SnPn/dt relationship between user symptoms (Sn) and allergens (P) over time (dt) and the user's information tracked over time;

employing the analysis engine to train machine learning models for increasing the user's pollen threshold, wherein the machine learning models are selected from support vector machines, k-nearest neighbors, random forests or mixtures thereof, wherein training comprises generating a tiered ensemble of the machine learning models using pooled data from users having environmental conditions similar to those of the target user, wherein the tiered machine learning models are each configured to receive target user input data and output a symptom level, and said outputs are combined using a weighted voting scheme to produce an overall prediction, where the voting weights are based on relative data impact; and

comparing factors specific to said user's behavior to the machine learning models, using the computerized processing device, to determine one or more treatment recommendation(s) for increasing the user's pollen threshold;

wherein the system recommends at least one allergy treatment intervention in real time;

wherein the system transmits the recommended allergy treatment intervention to the communication device; and

wherein the communication device provides in the app a personalized allergy impact profile for the user and the recommended allergy treatment for the user.