In this paper I exploit Google searches for the topics``symptoms '', `` unemployment"and``news '' as a proxy for how much attention people pay to the health and economic situation and the amount of news they consume, respectively . I then use an integrable nonautonomous Lotka-Volterra model to study the interactions among these searches in three U.S. States (Mississippi, Nevada and Utah), the District of Columbia and in the U.S. as a whole . I find that the results are very similar in all areas analyzed, and for different specifications of the model . Prior to the pandemic outbreak, the interactions among health searches, unemployment searches and news consumption are very weak, i.e . an increase in searches for one of these topics does not affect the amount of searches for the others . However, from around the beginning of the pandemic these interactions intensify greatly, suggesting that the pandemic has created a tight link between the health and economic situation and the amount of news people consume . I observe that from March 2020 unemployment predates searches for news and for symptoms . Consequently, whenever searches for unemployment increase, all the other searches decrease . Conversely, when searches for any of the other topics considered increase, searches for unemployment also increase . This underscores the importance of mitigating the impact of COVID-19 on unemployment to avoid that this issue swallows all others in the mind of the people.