<s>[INST] ### Project Overview
As part of a larger epidemiological study, a research center needs to monitor the rate of infections within a particular zip code or geographic area. An infection rate is considered to be "high" if it exceeds certain predetermined nuisance thresholds for that location. The thresholds varying by location reflects the fact that different areas have varying population density, demographics, and prevalence of certain health conditions that make them more or less susceptible to certain types of infection.

### Problem Description
Create an algorithm and data structures required to efficiently monitor the infection rates in multiple locations. The center should be able to add, remove, and query the current infection rate status for a given location and to alert if the rate is high or not. The algorithm should be efficient in terms of both time and space complexity.

### Proposed Solution: Binary Indexed Tree (BIT)
This problem can be solved using a Binary Indexed Tree (BIT, also known as a Fenwick tree) data structure, which can efficiently update and query prefix sums in an array. Our intention is to use the BIT to keep track of a running count of the number of infections at each location as well as calculate the cumulative infection counts across multiple locations, allowing us to calculate the rate of infection.

However, realizing that the infection rate needs to be compared with some specific location-dependent thresholds, we have to bring an additional data structure into play. To ensure efficient lookups and updates of these thresholds, we will use a hash map.

Though we can solve this problem using BIT, considering that updates and queries are more dominant than the threshold value lookups, we may consider a Segment Tree as an alternative data structure.

This problem seems more complex and requires the implementation of a data structure. How do you handle the organization and optimization of such a complex problem?[/INST]