The Traffic Light Model

The Traffic Light Model allows to treat local councils differently according to major infection indices, and decide on a policy to better cope with the crisis on the local level

The Model

An outline that maps red, orange, yellow, and green local councils throughout the country according to infection rate. This outline aims to help prevent the spread of the virus while imposing restrictions on areas where high infection levels have been recorded.

Each local council will receive a score between 0 and 10 weighing the following variables: The number of new patients per week per 10,000 residents, the rate of positive test results in each local council per week, and the rate of growth in the number of new patients in each locality per week.

The Rationale

The rules and restrictions will be based on predictions of the number of patients in the local council in two weeks' time. Therefore, if a high infection rate is expected, this could be curtailed by stricter restrictions. If the trend in the local council is acceptable, then a more open model will be allowed.

The Method

Each local council will receive a weekly index score consisting of the number of new patients, the percentage of positive test results, and the growth rate. In areas pertaining to citizens' daily life activities and routine, restrictions on that local council will be determined according to these indices. A national outline will be determined for areas that are part of the economy's essential operations as well as in areas with high risk potential.

Restrictions by Local Council

The local council is one of the foundations of public healthcare in emergency situations. Why is that actually? Because of the council's familiarity with its residents, with its unique characteristics, and with the establishments and businesses operating within its jurisdiction. The trust between the council and its residents and the capacity to continue maintaining this trust alongside councils' extensive scope of supervision generate the significant practical capacity to assist in breaking the chain of infection and increasing public compliance with the restrictions.

The plethora of local councils in Israel and their uniqueness and regional characteristics generate gaps in the manner and municipal treatment level vis-à-vis the crisis. These gaps generate a complicated situation assessment of the national infection map and necessitate the creation of an assessment and measurement model for how the pandemic is treated at the local level. "Magen Israel" plan includes stipulations for the development of a functional "Traffic Light" to be created together with local government and the Home Front Command. It will provide policymakers and the general public a situation assessment at the local level.

The "Traffic Light" allows differentiation between different councils regarding the leading infection indices, from which to draw a set of permits and incentives alongside restrictions. All these will allow for better coping with the aspects of this crisis on the local level. This "Traffic Light Model" will help local councils to proceed to positive index rates (green designation), all the while providing them with specific tools for treatment and assistance. Additionally, the "Traffic Light Model" will serve as a public information tool, providing all local agents with incentives and rousing them to action to step forward and be proactive.

How does it work?

The Traffic Light model classifies all local councils in Israel according to infection levels, according to which they are assigned a color designation and a set of restrictions. The formula for calculating the color designation is the number of new patients multiplied by the percentage of positive test results received and the infection growth rate. The rationale behind this formula is the attempt to predict future infection rates and to increase the number of tests as necessary. According to this plan, changes to the community's color designation will only occur if there is no significant decrease in the number of tests.